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Technical Product Manager - Data & ML Infrastructure

A stealth-mode AI startup is looking for a Technical Product Manager to join a team of engineers and researchers from Google and Frontier AI labs. You'll be leading the build and strategy of foundational systems that power machine learning and data-intensive applications, tackling problems where performance, reliability, and scale matter.

The Technical Product Manager will sit at the intersection of product, engineering, and AI research. They will define the roadmap for core data and ML infrastructure, work closely with engineering teams to translate ambitious technical goals into actionable plans, and engage directly with customers to ensure solutions solve high-impact problems.

This is not a typical PM role. The ideal candidate will need deep technical understanding of data systems, ML infrastructure, and pipelines, paired with the ability to prioritize and advocate relentlessly for customer needs. They’ll thrive in an environment where ambiguity is high, and technical decisions have immediate real-world impact.

The ideal candidate will have:

• 3+ Years' Product Management experience
• Strong technical background with experience in software engineering or ML systems
• Deep knowledge of data and ML infrastructure, including pipelines, storage, and compute
• A proven track record of working directly with customers to understand and solve real problems
• Excellent problem-solving and prioritization skills in fast-moving environments
• The ability to balance technical feasibility, user value, and long-term product vision

This role offers the chance to help shape the backbone of cutting-edge ML applications, working alongside a team with deep expertise in AI and engineering. It’s a high-ownership, high-impact role for someone who loves both building and defining technically complex products.

Job Title: Business Analyst -Pharma R&D

Location : SFO, CA (Onsite)

Client – Coforge / Genentech

C2C $70/Hr

Role Overview

Business Analyst with experience in Pharma R&D imaging systems, including radiology (DICOM) and digital pathology (non‑DICOM) platforms. The role focuses on requirements analysis, workflow definition, and validation documentation for image viewing, annotation, and ML‑enabled imaging solutions used in regulated clinical and research environments.

The Business Analyst acts as a bridge between R&D scientists, data scientists, ML validation specialists, quality teams, and IT, ensuring that business, clinical, and regulatory requirements

Job Description

Imaging Platform & Business Requirements

• Lead business and functional requirements gathering for radiology and digital pathology image viewing and annotation platforms used in Pharma R&D.
• Engage with R&D scientists, clinical operations, imaging SMEs, and CROs to understand workflows for image ingestion, review, labeling, and annotation.
• Define end‑to‑end requirements for image ingestion, data quality checks, metadata harmonization, viewing, annotation, and downstream analytics.
• Document as‑is / to‑be workflows, use cases, functional specifications, and non‑functional requirements (performance, usability, scalability, compliance).
• Work with Data Scientists and ML Validation Specialists to document business and clinical requirements for ML algorithms applied to imaging data with intended use of ML models in radiology and digital pathology workflows
• Ensure ML requirements align with GCP, FDA, and internal quality standards, without directly performing algorithm testing.
• Manage requirement changes and ensure traceability throughout the delivery and validation lifecycle.

Preferred Skills

• Prior experience working with Pharma companies or CROs on imaging or R&D data platforms.
• Strong documentation, communication, and cross‑functional stakeholder management skills.

Thanks

Govardhan

Your Trusted Digital Technology Partner

Email:

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:

Be a part of our success story. Launch offers talented and motivated people the opportunity to do the best work of their lives in a dynamic and growing company. Through competitive salaries, outstanding benefits, internal advancement opportunities, and recognized community involvement, you will have the chance to create a career you can be proud of. Your new trajectory starts here at Launch!

The Role:

Launch is actively seeking a visionary Solutions Architect / Principal Software Engineering Lead (AI) to design and deliver modern engineering and applied AI solutions across client engagements. This role blends deep hands‑on engineering, architectural leadership, AI system design, and client advisory. You will operate across system design, production‑grade engineering, multi‑agent architectures, cloud platform strategy, and the development of Launch’s AI practice.

Responsibilities Include:

Architecture & Technical Strategy

• Define the technical direction for client engagements end-to-end: discovery, design, build, and production hardening.
• Assess client technology ecosystems and identify high-impact opportunities for AI/ML integration.
• Lead architecture reviews, design sessions, and technology selection across cross-functional stakeholder groups.
• Translate ambiguous business problems into concrete engineering plans with clear scope, milestones, and risk callouts.

AI Engineering & Delivery

• Architect production agentic systems including multi-agent orchestration, agent harnesses, skill/tool composition, human-in-the-loop checkpoints, and inter-agent communication protocols (e.g., A2A, MCP).
• Build and govern MCP server ecosystems: design, deploy, and secure Model Context Protocol integrations connecting AI agents to enterprise data sources, internal APIs, and third-party platforms.
• Define agent skill and capability frameworks including reusable skill libraries, prompt engineering standards, and evaluation harnesses for consistent agent behavior across engagements.
• Architect RAG pipelines, fine-tuning workflows, and model lifecycle infrastructure (training, serving, experiment tracking) as foundational components of agentic systems.
• Integrate AI platforms and APIs (Azure OpenAI, Amazon Bedrock, Anthropic, Vertex AI) into production systems with enterprise-grade reliability, cost governance, and observability.
• Establish AI-native development practices: embed tools such as Claude Code, Cursor, and GitHub Copilot into team workflows with standards for AI-assisted code review, test generation, and documentation.
• Design evaluation and observability infrastructure including LLM eval frameworks, red-teaming, behavioral drift detection, and production monitoring across tool call chains, latency, and failure modes.
• Apply responsible AI governance: define guardrails, access controls, and audit patterns for agentic workflows in enterprise environments including scope containment and escalation paths.

Hands-On Engineering

• Write production code and lead by example — this role requires someone who is still close to the code.
• Design cloud-native architectures across multiple hyperscalers (AWS and Azure primarily) microservices, event-driven systems, serverless, and containerized workloads.
• Define and implement infrastructure-as-code using tools such as Terraform, Pulumi, CloudFormation, or Bicep.
• Design and optimize CI/CD pipelines, GitOps workflows, and container orchestration using Docker and Kubernetes.
• Establish observability and reliability practices using tools such as Datadog, Prometheus, Grafana, CloudWatch, or Azure Monitor.
• Drive security-by-design across the delivery lifecycle including IAM, network architecture, secrets management, and compliance automation.

Leadership & Client Advisory

• Lead engineering teams ranging from small squads to 10+ person delivery teams, scaling leadership approach to the needs of each engagement.
• Mentor and develop engineers at all levels through code reviews, pairing, and design coaching.
• Operate as a trusted advisor to client technical leadership and executive stakeholders. Communicate trade-offs clearly and build confidence.
• Influence without direct authority — driving alignment across cross-functional teams through technical credibility and stakeholder management.
• Lead discovery and requirements elicitation, surfacing the underlying business need beyond the stated request.
• Produce clear written artifacts: technical proposals, architecture decision records, SOWs, and executive-level status communication.
• Grow client relationships and identify follow-on opportunities through proposal contributions and delivery-driven account expansion.
• Contribute to Launch's growth — practice development, thought leadership, and hiring.

Qualifications:

Must-Haves:

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
• 10+ years in software engineering with demonstrated experience in architecture and technical leadership roles.
• 3+ years hands-on with AI/ML in production. Broad fluency across generative AI (LLMs, RAG, fine-tuning, agents), MLOps (model serving, pipelines, experiment tracking), and AI-integrated product development.
• Consulting or client-facing delivery experience with a proven ability to integrate into client organizations and establish credibility with technical and executive stakeholders.
• Full-stack engineering capability across frontend, backend, infrastructure, and data layers. Proficiency in multiple modern languages (e.g., Python, TypeScript/Node.js, C#/.NET, Java, or Go) with the ability to move between them as engagements require.
• Multi-hyperscaler depth across AWS and Azure, including their respective AI/ML service ecosystems (Bedrock, SageMaker, Azure OpenAI, Azure ML). GCP experience is a plus.
• Strong fundamentals in distributed systems, event-driven architecture, API design, and DevOps/platform engineering.
• Experience leading engineering teams in agile delivery environments.
• Business acumen with the ability to connect architecture decisions to cost, timeline, and organizational impact.
• Executive presence and communication skills effective with both technical and non-technical audiences.
• Proven ability to operate in ambiguous environments and adapt to diverse client cultures.

Strong Differentiators

• Experience contributing to the development of AI engineering practices, reusable frameworks, or internal accelerators within a consulting or enterprise environment.
• Experience advising C-suite or VP-level stakeholders on AI strategy, investment prioritization, and organizational readiness.
• Depth with agentic AI frameworks (LangChain, LangGraph, LangSmith, LlamaIndex, Semantic Kernel, CrewAI) and emerging standards like MCP (Model Context Protocol).
• Experience with enterprise data platforms (Databricks, Snowflake, BigQuery) in the context of AI/ML workloads.
• Cloud architecture certifications across AWS and Azure (AWS SA Professional, Azure Solutions Architect Expert).
• Published writing, open-source contributions, or conference speaking that demonstrates thought leadership in AI or software architecture.
• Domain depth in industries such as healthcare, financial services, retail, or public sector.

Compensation & Benefits:

As an employee at Launch, you will grow your skills and experience through a variety of exciting project work (across industries and technologies) with some of the top companies in the world! Our employees receive full benefits—medical, dental, vision, short-term disability, long-term disability, life insurance, and matched 401k. We also have an uncapped, take-what-you-need PTO policy. The anticipated base wage range for this role is $190,000 to $230,000. Education and experience will be highly considered, and we are happy to discuss your wage expectations in more detail throughout our internal interview process.

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)

The future of AI whether in training or evaluation, classical ML or agentic workflows starts with high-quality data.

At HumanSignal, we're building the platform that powers the creation, curation, and evaluation of that data. From fine-tuning foundation models to validating agent behaviors in production, our tools are used by leading AI teams to ensure models are grounded in real-world signal, not noise.

Our open-source product, Label Studio, has become the de facto standard for labeling and evaluating data across modalities from text and images to time series and agents-in-environments. With over 250,000 users and hundreds of millions of labeled samples, it's the most widely adopted OSS solution for teams working on building AI systems.

Label Studio Enterprise builds on that traction with the security, collaboration, and scalability features needed to support mission-critical AI pipelines powering everything from model training datasets to eval test sets to continuous feedback loops. We started before foundation models were mainstream, and we're doubling down now that AI is eating the world. If you're excited to help leading AI teams build smarter, more accurate systems we'd love to talk.

HumanSignal is looking for an exceptional Strategic Account Executive to drive growth with the world's most innovative AI companies. You'll be selling at the cutting edge: our Label Studio platform and Data Creation Laboratory services power the training data behind breakthrough AI applications at frontier labs and Fortune 500 enterprises.

This isn't traditional SaaS sales. Our customers are building the futureadvanced language models, autonomous systems, embodied AI, and applications that don't exist yet. They need purpose-built datasets manufactured from scratch, not scraped from the web. You'll be selling both our platform technology and our operational capability to create novel training data in controlled environments. The technical depth, deal complexity, and strategic importance of these relationships make this one of the most exciting sales roles in AI infrastructure.

You'll own relationships with AI leaders like Anthropic, OpenAI, Google DeepMind, Meta, Nvidia, Tesla, and others pushing the boundaries of what's possible. Your success will directly enable the next generation of AI breakthroughs.

You Will:

• Own strategic accounts: Drive the entire relationship with our most important AI customersfrom initial engagement through expansion and renewal
• Hunt and close new logos: Identify and win new customers among frontier AI labs, tech giants building AI capabilities, and innovative robotics companies
• Navigate complex organizations: Build deep relationships with executive stakeholders across engineering, ML research, product, and operations within customer organizations
• Drive revenue growth: Expand wallet share by identifying new use cases, additional business units, and opportunities to deepen our partnership
• Orchestrate internally: Lead cross-functional teams including delivery operations, engineering, product, and laboratory operations to develop winning strategies and flawless execution
• Be the customer advocate: Serve as the voice of the customer internally, influencing product roadmap and operational capabilities based on market needs
• Solve complex problems: Navigate technical requirements, custom data creation scenarios, and novel use cases that have never been done before
• Think strategically: Develop and execute comprehensive account plans that position HumanSignal as the long-term data infrastructure partner
• Close significant deals: Structure and negotiate contracts ranging from $500K to $5M+ with sophisticated technical and business buyers

Ideally You'd Have:

• 8+ years of enterprise sales or account management experience with a track record of exceeding quota
• 2+ years selling deeply technical products or services to both business and technical audiences (ML engineers, researchers, AI/ML leaders)
• Proven success closing complex, multi-stakeholder deals in the $500K-$5M+ range
• Experience in AI/ML, data infrastructure, cloud services, or other technical domains where you've sold to engineering and research teams
• Ability to understand technical concepts quickly and translate them into business value
• Strong consultative selling skills with ability to uncover needs, navigate ambiguity, and co-create solutions
• Executive presence and experience developing relationships with C-level stakeholders
• Track record of driving renewals and expansion within strategic accounts
• Excellent written and verbal communication skills, including creating executive-level materials
• Proficiency with modern sales tools (Salesforce, Outreach, Clari, LinkedIn Sales Navigator)
• Strong project management abilities and exceptional organizational skills
• Passion for AI and excitement about working at the frontier of what's possible

Nice to Haves:

• Technical background or degree in Computer Science, Engineering, or related field
• Experience selling services alongside software products
• Understanding of how training data impacts model performance
• Existing relationships within the AI research or frontier lab community
• Experience in fast-growing startups where you've helped build sales processes from scratch

Why This Role Is Special:

You're not selling commodity softwareyou're enabling the teams building AGI, autonomous vehicles, humanoid robots, and AI applications we can't even imagine yet. Every deal you close helps unlock new capabilities that could change the world. You'll work with the smartest people in AI, solve problems that have never been solved before, and build relationships with companies defining the future of technology.

About HumanSignal:

At HumanSignal, we're building the infrastructure for the next generation of AI. Our Label Studio platform powers data operations for leading organizations worldwide, while our Data Creation Laboratories manufacture the purpose-built datasets that breakthrough AI applications require.

We believe the next frontiers in AI won't be unlocked by scraping what's left on the webthey'll be built on human-created data that captures the complexity of how systems need to see, hear, reason, and react. Through controlled environments and operational excellence, we're enabling researchers and enterprises to innovate without being constrained by data availability.

We work with frontier AI labs, Fortune 500 enterprises, and government agencies who are pushing the boundaries of what's possible with AI. Join us in building the data that will build the future.

We are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity, or Veteran status. At HumanSignal we pay based on regional compensation market rate ranges across the globe. We are hiring for this role across North and South America as well as Europe. The base cash compensation range is $120,000 to $200,000 USD plus commission. These ranges are provided by market data and are in good faith. The final offer details are determined by several factors including candidate experience, expertise, as well as applicable industry knowledge and may vary from the pay ranges listed above.