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Company Description

PG Forsta is the leading experience measurement, data analytics, and insights provider for complex industries-a status we earned over decades of deep partnership with clients to help them understand and meet the needs of their key stakeholders. Our earliest roots are in U.S. healthcare -perhaps the most complex of all industries. Today we serve clients around the globe in every industry to help them improve the Human Experiences at the heart of their business. We serve our clients through an unparalleled offering that combines technology, data, and expertise to enable them to pinpoint and prioritize opportunities, accelerate improvement efforts and build lifetime loyalty among their customers and employees.

Like all great companies, our success is a function of our people and our culture. Our employees have world-class talent, a collaborative work ethic, and a passion for the work that have earned us trusted advisor status among the world's most recognized brands. As a member of the team, you will help us create value for our clients, you will make us better through your contribution to the work and your voice in the process. Ours is a path of learning and continuous improvement; team efforts chart the course for corporate success.

Our Mission:

We empower organizations to deliver the best experiences. With industry expertise and technology, we turn data into insights that drive innovation and action.

Our Values:

To put Human Experience at the heart of organizations so every person can be seen and understood.

• Energize the customer relationship:Our clients are our partners. We make their goals our own, working side by side to turn challenges into solutions.
• Success starts with me:Personal ownership fuels collective success. We each play our part and empower our teammates to do the same.
• Commit to learning:Every win is a springboard. Every hurdle is a lesson. We use each experience as an opportunity to grow.
• Dare to innovate:We challenge the status quo with creativity and innovation as our true north.
• Better together:We check our egos at the door. We work together, so we win together.

Don't meet every single requirement?Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. At Press Ganey we are dedicated to building a diverse, inclusive and authentic workplace, so if you're excited about this role but your past experience doesn't align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

Additional Information for US based jobs:

Press Ganey Associates LLC is an Equal Employment Opportunity/Affirmative Action employer and well committed to a diverse workforce. We do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, veteran status, and basis of disability or any other federal, state, or local protected class.

Pay Transparency Non-Discrimination Notice - Press Ganey will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information.

The expected base salary for this position ranges from $100,000 to $140,000. It is not typical for offers to be made at or near the top of the range. Salary offers are based on a wide range of factors including relevant skills, training, experience, education, and, where applicable, licensure or certifications obtained. Market and organizational factors are also considered. In addition to base salary and a competitive benefits package, successful candidates are eligible to receive a discretionary bonus or commission tied to achieved results.

All your information will be kept confidential according to EEO guidelines.

Our privacy policy can be found here:legal-privacy/

Our client is a forward-thinking consultancy at the forefront of technology and innovation, dedicated to empowering organizations to thrive in an ever-evolving digital landscape. Our clients mission is to help businesses harness the power of data and artificial intelligence to drive growth, enhance customer experiences, and optimize operations.

As they enter their next phase of expansion, they are seeking a GenAI Architect to drive cutting-edge AI/ML projects and turn complex data into actionable insights. You’ll thrive in a client-facing, project-based role, leading teams to design, build, and deploy enterprise-scale models across diverse use cases.

Responsibilities

• Lead project teams and act as the technical architect on AI/ML initiatives.
• Analyze large, complex datasets and translate them into impactful insights using ML and AI techniques.
• Build, implement, and deploy models into production environments, leveraging best practices across cloud and on-prem solutions.
• Collaborate with clients to define requirements, architect solutions, and present technical recommendations.
• Coach and mentor junior team members while driving project success.

Experience

• Proven track record building large-scale AI/ML solutions in enterprise or consulting environments.
• 5+ years of hands-on experience in statistical modelling, analytics, and machine learning.
• Advanced academic background (Master’s in Statistics, Math, Computer Science, or related; PhD is a strong plus).
• Expertise in a wide range of ML and AI techniques: regression models, cluster analysis, predictive modeling, neural networks, deep learning, decision trees, ensemble methods, and more.
• Strong programming skills in Python, TensorFlow, PyTorch, Hugging Face Transformers, and other ML frameworks for model development and experimentation.
• Deep understanding of NLP fundamentals, including tokenization, embeddings, language modeling, sequence labeling, and text generation.
• Experience with modern AI tooling: LangChain, vector databases, prompt engineering, and large-scale data embedding.
• Solid knowledge of relational (SQL) and non-relational (NoSQL) databases, and distributed systems like Hadoop and Spark.
• Ability to turn complex datasets into compelling, actionable insights using visualization tools (RShiny, Python, Tableau, Power BI, D3.js, etc.).
• Passion for mentoring and leading teams, fostering growth and knowledge sharing.
• Experience managing data workflows: wrangling, exploring, transforming, and analyzing diverse datasets.
• Familiarity with monitoring model performance, tuning, and ensuring high-quality data.
• Excellent communication skills with the ability to engage stakeholders at both technical and business levels.
• Strong curiosity and thought leadership, staying ahead of AI/ML trends and research.
• Experience deploying ML models into production on cloud platforms such as Google Cloud Platform, Azure, or AWS.

Please note, our client is unable to support visa transfers at this stage and can hire US citizens or Greencard holders.

If you feel you have the required skills, please get in touch with a copy of your CV! Or email me directly at

At Quotacom, we take the security and privacy of your personal data very seriously, any data we hold will be in accordance with data protection legislation. Full details of our privacy notice can be found at

Fractal is a strategic AI partner to Fortune 500 companies, with a bold vision: to power every human decision in the enterprise. We believe the future belongs to organizations that combine human imagination with intelligent systems—and Fractalites are the ones building that future. As we scale our Technology, Media & Telecom (TMT) practice in the United States, we are looking for a senior, client-facing Head of Engineering to shape and deliver world-class Data & AI platforms for leading Technology, Media & Telecom organizations.

This is not a back-office engineering role. This is a consulting-led, client-facing engineering leadership position for someone who is equally comfortable whiteboarding architecture with principal engineers, rolling up their sleeves with delivery teams, and advising CIOs, CTOs, and CDOs in the boardroom.

Learn more at Fractal | Intelligence for Imagination.

Note: This position is not eligible for Immigration Sponsorship at this time.

About the Role

This is a four-axis leadership role requiring technical depth, executive presence, team leadership, and embedded delivery. You'll work directly with top technical and functional leaders at some of the largest TMT companies in the world.

As Head of Engineering for Fractal's Technology, Media & Telecom (TMT) vertical, you will personally shape the architecture of mission-critical AIML platforms, often in first-party tech stack, and develop/drive the team of ICs who bring them to life.

Responsibilities

Some engagements will look like a traditional advisory model. Others will look a lot more like Forward Deployed Engineering: your team embedded inside a client's engineering org, working within their first-party tech stack, shipping production code alongside their engineers, and earning influence through technical credibility, not org chart position.

You will need to be in the room when the technology roadmap needs to change. When a business pivot, a new regulation, or a technology shift forces a rethink mid-execution, you are the person who picks up the marker, walks to the whiteboard, and redraws the architecture in real time, credibly, for the CTO, and Principal Engineering leaders simultaneously.

Technical Depth (Hands-On Architecture)

• Own AI/Data platform architecture decisions — from Lakehouse design and real-time streaming to MLOps, LLMOps, and AgentOps pipelines in production
• Serve as the technical authority for Fractal's TMT engineering practice — defining standards, reviewing design, and holding the bar on reliability, scalability, and security
• Translate ambiguous business problems into concrete, buildable platform architectures — and stay close enough to execution to know when something is not working
• Drive the industrialization of GenAI: moving clients from proof-of-concept to enterprise-grade, governed, and observable AI systems

Executive Presence & Live Architectural Thinking

• Command the room with senior client leadership — CIOs, CTOs, CDOs, and their direct reports - as a peer, not a vendor
• Whiteboard new architectural directions on the spot: when a business pivot, acquisition, regulatory shift, or technology breakthrough forces a mid-execution rethink, you synthesize it into a credible, buildable path forward live, in the room, without needing a week to prepare a deck
• Translate between two worlds simultaneously: make the architecture legible to a CFO and rigorous enough to satisfy a principal engineer in the same session
• Shape client roadmaps at the strategic level; identifying where the current plan is under-ambitious, over-engineered, or misaligned with emerging AI capabilities, and steering accordingly
• Represent Fractal at the highest level of client relationship

Team Leadership (Building & Driving Senior ICs)

• Develop and lead a high-performing group of individual contributors. principally senior and staff engineers, ML engineers, and data platform engineers
• Create the engineering culture: rigorous delivery standards, architectural thinking, and a bias toward elegant, production-grade solutions over quick fixes
• Build leadership depth within the team, identifying principals who can own programs and grow into broader roles
• Partner across Fractal's global AI and engineering Capability functions to staff programs strategically and raise capability across the TMT practice

Forward-Deployed & Embedded Delivery

• Lead and run FDE-style engagements where your team operates inside the client's engineering environment
• Navigate and deliver within client-owned, first-party technology stacks: proprietary data platforms, internal ML infrastructure, custom orchestration systems, and bespoke toolchains that do not appear in any industry survey
• Adapt quickly to non-standard environments, understanding a client's internal platform deeply enough to extend it, integrate into it, and earn the trust of their engineering staff
• Balance the tension between what Fractal does best and what the client's stack demands, knowing when to bring pattern, when to adapt, and when to advocate for a better path
• Set the standards for how Fractal operates in deeply embedded engagements: how we onboard, document, transfer knowledge, and leave clients stronger than we found them

Candidate Profile

Technical Qualifications

TMT clients bring genuinely hard problems on both open and proprietary infrastructure. Expect to architect and oversee:

• GenAI systems: RAG architectures, LLM fine-tuning pipelines, agentic workflow orchestration, and LLMOps observability
• AI-powered products: personalization engines, churn prediction, content recommendation, and network fault detection
• Client-proprietary ML infrastructure: internal feature stores, custom model serving layers, bespoke experiment tracking systems, and first-party orchestration frameworks
• Cloud-native infrastructure across AWS, Azure, and GCP with enterprise-grade governance, security, and compliance baked in
• Real-time and event driven data pipelines (e.g. network telemetry)
• Modern Lakehouse platforms (Databricks, Snowflake, Delta Lake, Iceberg) at petabyte scale and proprietary data platform equivalents at leading tech-forward TMT organizations

Non-technical Qualifications

We are particularly interested in leaders from environments where engineering rigor, client accountability, executive presence, and AI depth all coexist including Forward Deployed Engineering, elite data/ML platform teams, and senior hyperscaler architecture practices.

• 15–20 years of experience spanning AI/data engineering and technical leadership with clear evidence of owning architecture at scale
• Deep hands-on experience deploying AI/ML/GenAI systems in production, in addition to advising on them
• Demonstrated executive presence: you have walked into a CTO or CDO review, redrawn the architecture based on new constraints, and left the room with alignment
• The ability to whiteboard fluently under pressure, synthesizing a team's in-flight work with a new business direction, making it rigorous enough for engineers and clear enough for executives, on the spot and without a rehearsal
• Experience operating within client-owned or non-standard technology stacks - you have learned a proprietary system, earned trust from skeptical internal engineers, and delivered production-grade results inside someone else's infrastructure
• A track record of leading senior engineers and building high-performance ML/engineering teams, including hiring, coaching, and developing principal-level ICs
• Direct executive engagement experience - you have influenced CIO/CTO/CDO decisions and can hold your own in a room with technical and non-technical stakeholders at once
• Strong cloud-native fluency across one or more hyperscalers, with genuine depth in data platform patterns (streaming, batch, Lakehouse, governance)

Strong Preferences

• Experience in TMT vertical — hi-tech, telco, media platforms, streaming infrastructure, ad tech, or content delivery at scale
• Prior work in FDE-style or embedded delivery models where your team shipped inside a client codebase and was evaluated by their engineering standards, not just deliverable milestones
• Comfort with the ambiguity of 1P stack environments: you have debugged undocumented internal tools, extended proprietary frameworks, and figured out how to make external expertise land inside a closed ecosystem
• A personal reputation for architectural clarity: the person colleagues call when a problem needs to be drawn, not just describe
• Contributions to the ML/AI community: open source, publications, conference talks, or influential architectural patterns

Who Thrives Here

The Fractalite mindset is curious, rigorous, and impact driven. You will thrive in this role if you:

• Enjoy being client-facing and accountable for outcomes.
• Are comfortable navigating ambiguity, scale, and complex stakeholder environments.
• Believe great platforms come from strong engineering culture plus disciplined execution.
• See AI not as a novelty, but as a core enterprise capability that must be engineered responsibly.

Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

• 
  
• Provide deep technical leadership to a portfolio of high impact data science initiatives involving sales and advisor experience. Identify the optimal sets of data, models, training, and testing techniques required for successful product delivery. Remove complex technical impediments
  
• Leverage your experience and skills to identify new opportunities where data science and AI can improve experiences, gain efficiencies, and generate sales.
  
• Manage team members in AI/ML and model development, testing, training, and tuning. Apply hands-on experience to ensuring best-in-class model development. Mentor team members in technical skill development and product ownership.
  
• Communicate clearly and concisely, in writing and verbally, all facets of model design and development. Continuously look for insights in models developed and generate new ideas for model improvement.
  
• Manage external vendors in the execution of parts of the data science development process as needed.
  
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code on Prudential's AI/ML platform.
  
• Bring a deep understanding of relevant and emerging technologies, give technical direction to team members and embed learning and innovation in the day-to-day.
  
• Work on significant and unique issues where analysis of situations or data requires an evaluation of intangible variables and may impact future concepts, products or technologies.
  
• Familiarity with Python, SQL, AWS, and JIRA.
  
• Familiarity with LLMs, deployment of LLMs, RAG, LangChain, LangGraph, and Agentic AI concepts.
  

• 
  
• Applied Statistics, Computer Science, or Engineering or experience in related fields with a focus on machine learning, AI, and LLMs.
  
• Junior category industry experience with responsibility for developing and delivering advanced quantitative, AI/ML, analytical and statistical solutions.
  
• Ability to lead a small team with minimal guidance and effectively leverage diverse ideas, experiences, thoughts and perspectives to the benefit of the organization to deliver AI products.
  
• Ability to influence business stakeholders and to drive adoption of AI/ML solutions.
  
• Experience with agile development methodologies, Test-Driven Development (TDD), and product management.
  
• Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company's business
  
• Demonstrated ability to mentor and operational management of data science team based on project requirements, resourcing requirements, and planning dependencies as appropriate, anticipate risks and bottlenecks and proactively takes actions
  
• Excellent problem solving, communication and collaboration skills, and stakeholder management
  
• Significant experience and/or deep expertise with several of the following:
  
• Machine Learning and AI: Understanding of machine learning theory, including the mathematics underlying machine learning algorithms. Expertise in the application of machine learning theory to building, training, testing, interpreting and monitoring machine learning models. Expertise in traditional machine learning models (unsupervised, XGBoost, etc.) and Large Language Models (OpenAI, Claude).
  
• Model Deployment: Understanding of model development life cycle, CI/CD/CT pipelines (using tools like Jenkins, CloudBees, Harness, etc.), A/B testing, and pipeline frameworks such as AWS SageMaker, and newer AWS/Azure Agentic AI infrastructure products.
  
• Data Acquisition and Transformation: Acquiring data from disparate data sources using APIs and SQL. Transform data using SQL and Python. Visualizing data using a diverse tool set including but not limited to Python.
  
• Database Management Systems: Knowledge of how databases are structured and function in order use them efficiently. May include multiple data environments, cloud/AWS, primary and foreign key relationships, table design, database schemas, etc.
  
• Data Analysis and Insights: Analyzing structured and unstructured data using data visualization, manipulation, and statistical methods to identify patterns, anomalies, relationships, and trends.
  
• Programming Languages: Python and SQL
  

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other's unique experiences and embrace theflexibility to do your best work. Creating a career you love? It's Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we're looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we'll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

With more than 500 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers build personalized experiences to help Pinners create a life they love. With just over 3,000 global employees, our teams are small, mighty, and still growing. At Pinterest, you'll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won't find anywhere else.

What you'll do:

• 
  
• Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
  
• Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas
  
• Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
  
• Work in a high-impact environment with quick experimentation and product launches
  
• Keeping up with industry trends in recommendation systems
  

What we're looking for:

• 
  
• 4+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
  
• End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
  
• Degree in computer science, machine learning, statistics, or related field
  
• Nice to have:
  
  • 
    
  • Publications at top ML conferences
    
  • Expertise in scalable realtime systems that process stream data
    
  • Passion for applied ML and the Pinterest product
    
  • MS/PhD in Computer Science, ML, NLP, Statistics, Information Sciences, related field, or equivalent experience.
    
  
  
  

Relocation Statement:

• 
  
• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
  

#LI-SA1

#LI-REMOTE

At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.

Information regarding the culture at Pinterest and benefits available for this position can be found here.

Our Commitment to Inclusion:

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other's unique experiences and embrace theflexibility to do your best work. Creating a career you love? It's Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we're looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we'll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

The Content Understanding team builds machine learning models that "read" Pinterest content-images, text, and video-to produce high-quality semantic signals (e.g., embeddings, localization, quality/safety labels). These signals power relevance and retrieval for Homefeed, Search, Related Pins, and Ads, and also support integrity use cases like spam and low-quality detection. We work end-to-end: from data and labeling strategy, to model training and evaluation, to low-latency serving and monitoring at Pinterest scale. The role is ideal for a senior modeler who also enjoys developing, productionizing models and leading technical direction across teams.

What you'll do:

• 
  
• Lead modeling strategy for content understanding (vision, NLP, multimodal), including architecture selection, training approach, and evaluation methodology.
  
• Design and ship production models that generate content signals such as embeddings and classifications used across multiple product surfaces.
  
• Own the full ML lifecycle: data/labeling strategy (human labels + weak supervision), training pipelines, offline evaluation, online experimentation, deployment, and monitoring/retraining.
  
• Partner with infra/platform teams to ensure scalable, reliable training/serving (latency, cost, observability, rollout safety).
  
• Collaborate with signal-consuming teams (ranking, retrieval, integrity, ads) to define signal contracts, adoption patterns, and success metrics.
  
• Provide technical leadership through design reviews, mentoring, and raising the quality bar for modeling and ML engineering practices.
  

What we're looking for:

• 
  
• M.S/ PhD degree in Computer Science, Statistics or related field.
  
• Significant industry experience building software and ML pipelines/systems, including technical leadership (project/tech lead or equivalent).
  
• Strong proficiency in Python and at least one ML stack such as PyTorch / TensorFlow, plus solid software engineering fundamentals.
  
• Proven experience training and deploying ML models to production, including model versioning, rollouts, monitoring, and retraining strategies.
  
• Deep hands-on experience in content understanding domains, such as:
  
  • 
    
  • computer vision (classification, detection, representation learning),
    
  • NLP (text classification, entity/topic modeling),
    
  • multimodal / embedding models (e.g., transformer-based representations).
    
  
  
• Experience working with large-scale datasets and distributed compute (e.g., Spark-like ecosystems, distributed training, GPU environments).
  
• Strong applied skills in evaluation and experimentation: defining metrics, offline/online alignment, A/B testing, debugging regressions, and model quality analysis.
  
• Demonstrated ability to influence across teams and drive ambiguous problem areas to measurable outcomes.
  

Relocation Statement:

• 
  
• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
  

In-Office Requirement Statement:

• 
  
• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
  
• This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.
  

#LI-REMOTE

#LI-SM4

At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.

Information regarding the culture at Pinterest and benefits available for this position can be found here.

Our Commitment to Inclusion:

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other's unique experiences and embrace theflexibility to do your best work. Creating a career you love? It's Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we're looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we'll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

About tvScientific

tvScientific is the first and only CTV advertising platform purpose-built for performance marketers. We leverage massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Our solution combines media buying, optimization, measurement, and attribution in one, efficient platform. Our platform is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification who have now purpose-built a CTV performance platform advertisers can trust to grow their business.

As a Sr. Machine Learning Engineer at tvScientific, you'll build the ML and AI systems behind our Connected TV ad-buying platform: real-time bidding, campaign optimization, and incrementality measurement at scale. We're an adtech company solving a hard problem: making CTV advertising actually measurable. Our platform helps advertisers buy ads across the CTV ecosystem: Hulu, Pluto TV, Disney+, HBO Max, and hundreds of FAST channels: and prove that those ads drove real business outcomes.

What you'll do:

• 
  
• Write production Python that powers real-time bidding, model training, and campaign optimization
  
• Train, deploy, and monitor ML models that decide which ads to show, when, and at what price: millions of bid decisions per second
  
• Build and improve our incrementality measurement systems: helping advertisers understand the true causal lift of their CTV spend
  
• Design and implement new ML products across the ad-buying lifecycle: audience targeting, bid optimization, pacing, and attribution
  
• Use LLMs and generative AI to build internal tools that accelerate how we develop, test, and ship ML systems
  
• Serve as a technical lead and mentor on a distributed engineering team
  

What we're looking for:

• 
  
• Strong production Python skills: you write code that runs in prod, not just notebooks
  
• Solid statistics and ML fundamentals: you can reason about experiment design, model evaluation, and when simpler approaches beat complex ones
  
• Familiarity with modern AI tools and good judgment about where they add value
  
• Adtech or CTV experience: familiarity with RTB, programmatic advertising, supply-path optimization
  
• Clear written communication: we're a distributed team and writing is how decisions get made
  
• Comfort with ambiguity: you'll own problems end-to-end in a fast-moving environment, from scoping to shipping
  
• Nice-to-Haves:
  
  • 
    
  • Teaching experience
    
  • Causal inference: uplift modeling, synthetic controls, difference-in-differences, or incrementality testing
    
  • Big data experience with Scala and Spark
    
  • Systems programming experience in Zig or similar (C, C++, Rust)
    
  • Reinforcement learning or bandit algorithms in production
    
  • Experience building agentic AI systems or LLM-powered workflows
    
  • MLOps experience: model deployment, monitoring, and pipeline orchestration on AWS
    
  
  
  

In-Office Requirement Statement:

• 
  
• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
  

Relocation Statement:

• 
  
• This position is not eligible for relocation assistance. Visit ourPinFlexpage to learn more about our working model.
  

#LI-SM4

#LI-REMOTE

At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.

Information regarding the culture at Pinterest and benefits available for this position can be found here.

Our Commitment to Inclusion:

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other's unique experiences and embrace theflexibility to do your best work. Creating a career you love? It's Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we're looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we'll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

With more than 500 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers build personalized experiences to help Pinners create a life they love. With just over 4,000 global employees, our teams are small, mighty, and still growing. At Pinterest, you'll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won't find anywhere else.

Within the Monetization ML Engineering team, we try to connect the dots between the aspirations of Pinners and the products offered by our partners. In this role, you will be responsible for developing and executing a vision for the evolution of the machine learning technology stack within Ads.

What you'll do:

• 
  
• Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
  
• Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas
  
• Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
  
• Work in a high-impact environment with quick experimentation and product launches
  
• Keep up with industry trends in recommendation systems
  
• Leverage LLMs to enhance content understanding
  

What we're looking for:

• 
  
• 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
  
• Degree in computer science, statistics, or related field; or equivalent experience
  
• End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
  
• Practical knowledge of large scale recommender systems, or modern ads ranking, retrieval, targeting, marketplace systems
  
• Nice to have:
  
  • 
    
  • M.S. or PhD in Machine Learning or related areas
    
  • Publications at top ML conferences
    
  • Expertise in scalable realtime systems that process stream data
    
  • Passion for applied ML and the Pinterest product
    
  • Background in computational advertising
    
  
  
  

Relocation Statement:

• 
  
• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
  

#LI-HYBRID

At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.

Information regarding the culture at Pinterest and benefits available for this position can be found here.

Our Commitment to Inclusion:

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other's unique experiences and embrace theflexibility to do your best work. Creating a career you love? It's Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we're looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we'll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

With more than 600 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers build personalized experiences to help Pinners create a life they love. With just over 4,000 global employees, our teams are small, mighty, and still growing. At Pinterest, you'll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won't find anywhere else.

Within the Monetization ML Engineering team, we try to connect the dots between the aspirations of Pinners and the products offered by our partners. In this role, you will be responsible for developing and executing a vision for the evolution of the machine learning technology stack within Ads.

What you'll do:

• 
  
• Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
  
• Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas
  
• Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
  
• Work in a high-impact environment with quick experimentation and product launches
  
• Keep up with industry trends in recommendation systems
  
• Leverage LLMs to enhance content understanding
  

What we're looking for:

• 
  
• 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
  
• Degree in computer science, machine learning, statistics, or related field
  
• End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
  
• Practical knowledge of large scale recommender systems, or modern ads ranking, retrieval, targeting, marketplace systems
  
• Nice to have:
  
  
  • 
    
  • Publications at top ML conferences
    
  • Expertise in scalable realtime systems that process stream data
    
  • Passion for applied ML and the Pinterest product
    
  • Background in computational advertising
    
  
  
  

Relocation Statement:

• 
  
• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
  

#LI-HYBRID

#LI-SM4

At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.

Information regarding the culture at Pinterest and benefits available for this position can be found here.

Our Commitment to Inclusion:

The Product Manager is responsible for the product planning and execution throughout the Product Lifecycle, including gathering and prioritizing product and customer requirements, defining the product vision, and ensuring revenue and customer satisfaction goals are met. The Product Manager’s job also includes ensuring that the product supports the company’s overall strategy and goals.

This role supports an eCommerce fulfillment environment that manages pickup, third-party delivery (Instacart and DoorDash), and operations. The team is building a platform focused on order submission, selection, and routing, with an emphasis on operational reporting, process optimization, and demand forecasting.

About the Role

The Product Manager is responsible for the product planning and execution throughout the Product Lifecycle, including gathering and prioritizing product and customer requirements, defining the product vision, and ensuring revenue and customer satisfaction goals are met.

Responsibilities

• Manage all technical aspects of product through product lifecycle
• Work directly and indirectly with business stakeholders, vendors and third parties to ensure execution of deliverables
• Create, maintain and communicate product catalog and technology roadmaps, including near-term delivery, to engage stakeholders across the organization
• Identify, measure and improve key product catalog metrics to enhance the customer experience, and create a compelling, relevant product vision using web metrics, customer insights, feedback, research and internal operational metrics
• Elicit, define and analyze medium to complex requirements in various formats ensuring they are testable, measurable and traceable
• Set criteria for minimum viable product to increase the speed/frequency with which enhancements and new capabilities are delivered
• Lead the appropriate teams to refine, prioritize and manage requirements using various tools (e.g., templates, team backlogs, requirements management or agile task management applications)
• Lead requirement walk-throughs with key stakeholders using various methods (e.g., team demos, workshops, sprint planning and backlog refinement sessions)
• Identify and estimate anticipated work efforts based on priority using requirement work plans, program increment (PI) planning, and sprint planning
• Define and resolve dependencies, issues and risks and identify impacted areas through team collaboration
• Break down a medium to complex vision into smaller projects, initiatives or features

Qualifications

Skills: Must-Have

• Product strategy & prioritization
• Data platform fundamentals
• ML literacy
• Stakeholder communication
• Designing for expert users without alienating new ones
• Clear documentation and onboarding flows
• Understanding user workflows—not just APIs

Strong Differentiators

• MLOps understanding
• Experimentation and metrics fluency
• Responsible AI leadership
• Platform UX thinking
• Stakeholder Management

Required Skills

• Align business leaders, engineers, data scientists, legal/compliance, and ops
• Translate technical constraints into business-relevant language
• Manage expectations around ML uncertainty and iteration

Preferred Skills

• Data Concepts You Should Be Fluent In
• Data types: structured, semi-structured, unstructured
• Data pipelines (batch vs. streaming)
• Data quality dimensions: accuracy, completeness, timeliness
• Data lineage and observability
• Metadata, schemas, and versioning
• Platform Thinking
• APIs, SDKs, and self-service capabilities
• Multi-tenant vs. single-tenant design
• Performance, scalability, and cost tradeoffs
• Internal vs. external (customer-facing) platforms
• Machine Learning Fundamentals Every PM Should Know
• Supervised vs. unsupervised learning
• Training vs. inference
• Features, labels, and training data
• Model evaluation metrics (precision, recall, AUC, RMSE, etc.)
• Overfitting vs. generalization
• ML Product Realities
• ML outputs are probabilistic, not deterministic
• Model performance degrades over time (data drift, concept drift)
• Improving models often requires better data, not better algorithms
• ML development is experimental and iterative
• Areas that must be understood
• Model training pipelines
• Model deployment patterns (batch, real-time, edge)
• Model monitoring and retraining
• Versioning of models and data
• Rollbacks and experimentation (A/B tests, canary releases)