Simplify, AI Jobs in Usa

Position Overview

The Technical AI Project Manager plays a critical role in driving the successful delivery of Artificial Intelligence (AI) projects. This position bridges the gap between technical AI development teams and business stakeholders, ensuring that AI solutions are delivered on time, within scope, and aligned with organizational objectives. The ideal candidate combines strong project management skills with technical knowledge of AI methodologies, tools, and best practices.

Key Responsibilities

Lead end-to-end planning, execution, and delivery of AI and machine learning projects, ensuring alignment with business goals and technical feasibility.

Collaborate with cross-functional teams including data scientists, software engineers, product managers, and business analysts to define project scope, objectives, and deliverables.

Develop detailed project plans, manage schedules, allocate resources, and monitor progress to ensure timely delivery of project milestones.

Identify potential risks, develop mitigation strategies, and proactively resolve issues that may impact project timelines or quality.

Translate complex technical requirements and AI concepts into clear, actionable tasks for both technical and non-technical audiences.

Oversee the implementation of AI models, data pipelines, and system integrations, ensuring adherence to best practices in software development and AI ethics.

Manage project budgets, track expenditures, and report on project status to stakeholders and senior management.

Ensure compliance with data security, privacy regulations, and organizational policies throughout the AI project lifecycle.

Drive continuous improvement by capturing lessons learned and sharing best practices across the organization.

Qualifications

Bachelor’s or Master’s degree in a related field

3+ years of experience managing technical projects, preferably in AI, machine learning, or data analytics domains.

Strong understanding of AI/ML concepts, and software development life cycle.

Proven ability to manage multiple projects simultaneously in a fast-paced, agile environment.

Excellent organizational, communication, and leadership skills.

Familiarity with cloud platforms (e.g., Azure, AWS, Google Cloud) and MLOps tools is a plus.

Project Management Professional (PMP), Agile, or Scrum certification is desirable.

Key Competencies

Technical acumen in translating requirements for the AI team

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 seeking a Quality Control Lead to ensure world-class data quality across our Label Studio platform and Data Creation Laboratory operations. In this role, you will be the ultimate authority on the integrity and usefulness of the data we manufacture and deliver to customers. You will drive accountability across our highest-impact projectstackling complex quality challenges head-on while leading strategic initiatives that enable reliable, scalable delivery as we grow.

Our Data Creation Laboratories don't just label existing datawe manufacture purpose-built datasets from scratch in controlled environments. This means quality control takes on new dimensions: you're not just checking annotations, you're ensuring that the human-generated data we create meets the exacting standards required by frontier AI labs and enterprises pushing the boundaries of what's possible.

You Will:

• Build and lead a high-performing team of quality control specialists who set the standard for data excellence
• Collaborate with delivery teams and laboratory operations to interpret, refine, and exceed customer requirements
• Manage day-to-day operations including workload planning, resource allocation, and performance reporting
• Design and implement scalable quality frameworks and best practices that grow with the organization
• Champion initiatives that drive improvements in quality, operational efficiency, and customer satisfaction
• Serve as the ultimate standard-bearer for data quality, ensuring HumanSignal is recognized as the most trusted source of training data in AI

Ideally You'd Have:

• Bachelor's degree in Computer Science, Engineering, Operations, or a related discipline
• 4+ years of experience leading teams or scaling complex operational or technical processes
• Strong analytical capabilities and problem-solving skills, with proficiency in SQL and meticulous attention to detail
• Demonstrated experience building systems, processes, and teams across diverse customer segments or product offerings
• Hands-on background in operations management, product management, or management consulting at a top-tier firm
• Track record of taking initiative and driving results in cross-functional technical environments

Nice to Haves:

• Master's degree in Computer Science, Engineering, Operations, or a related field
• Experience in AI/ML data operations or services
• Background working in high-growth, dynamic startup environments
• Experience building and scaling teams from the ground up

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 $90,000 to $140,000 USD. 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.

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.

We are seeking a Machine Learning Engineer to build out our simulation and AI capabilities. You'll design and implement systems that model the CTV advertising ecosystem - auction dynamics, bidding strategies, campaign outcomes, and counterfactual scenarios - and develop AI-driven tools that accelerate how we build, test, and deploy ML systems.

What you'll do:

• 
  
• Design and build simulation environments that model CTV auction mechanics, inventory supply, and advertiser competition
  
• Develop counterfactual and what-if frameworks for evaluating bidding strategies, budget allocation, and pacing algorithms offline
  
• Build AI agents that explore strategy spaces, generate hypotheses, and automate experimentation within simulated environments
  
• Use LLMs and generative AI to accelerate internal ML workflows - synthetic data generation, code generation, automated analysis, and rapid prototyping
  
• Use simulation to de-risk ML model deployments - validate new bidding and optimization strategies before they touch live traffic
  
• Define the technical direction for simulation and AI infrastructure and mentor engineers on the team
  

What we're looking for:

• 
  
• Strong production Python skills and experience building simulation or modeling systems
  
• Deep understanding of probabilistic modeling, stochastic processes, or agent-based simulation
  
• Hands-on experience with modern AI tools: LLMs, code generation, agentic workflows - and good judgment about when they help vs. when they don't
  
• Adtech experience: you understand auction theory, RTB mechanics, and the dynamics of programmatic advertising
  
• Ability to translate business questions ("what happens if we change our bid strategy?") into rigorous simulation frameworks
  
• Clear written communication: you'll be defining new technical directions and need to bring others along
  
• Ownership: you scope, design, and ship systems end-to-end with minimal direction
  
• Nice-to-Haves:
  
  • 
    
  • Causal inference - uplift modeling, synthetic controls, difference-in-differences, or incrementality testing
    
  • Experience with discrete event simulation, Monte Carlo methods, or digital twins
    
  • Reinforcement learning - using simulated environments for policy learning and evaluation
    
  • Experience building agentic AI systems or multi-agent simulations
    
  • Big data experience with Scala and Spark
    
  • Systems programming experience in Zig or similar (C, C++, Rust)
    
  • 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:

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• This position is not eligible for relocation assistance. Visit our PinFlex page 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:

Open date: March 5, 2026

Next review date: Friday, Mar 20, 2026 at 11:59pm (Pacific Time)

Apply by this date to ensure full consideration by the committee.

Final date: Friday, Apr 10, 2026 at 11:59pm (Pacific Time)

Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.

The Chang Lab, led by PI Serina Chang, is part of the Berkeley EECS Department, the UCSF UC Berkeley Joint Program in Computational Precision Health, the Berkeley AI Research (BAIR) Lab, and the Center for Human-Compatible AI (CHAI).

We are seeking a postdoc to work on projects at the intersection of AI and human behavior, including modeling human behavior with AI, building AI tools to support societal decision-making, and studying the impacts of AI on society. Potential projects include:

1. 
   
2. Developing AI models of individuals that can accurately simulate complex behaviors across modalities, supported by rich, individual-level data including mobility trajectories, surveys, in-depth interviews, and health behaviors and outcomes.
   
3. Scaling simulations to entire societies with millions of agents, developing robust validation of societal simulations, and applying societal simulations to inform decisions in high-stakes domains (e.g., public health, emergency response).
   
4. Studying the impacts of generative AI on human health and well-being, from individual-level human-AI interactions (e.g., mental health risks, health decision-making) to community-level dynamics (e.g., changing support networks, engagement with health systems).
   

The selected candidate will assist with leading research projects, work closely with PI Serina Chang, mentor junior students in the lab, and help with the management of the lab.

Union: resources/employment-policies-contracts/bargaining-units/postdoctoral-scholars/contract/

PhD degree or equivalent international degree, or enrolled in a PhD or equivalent international degree granting program

PhD degree or equivalent international degree

• 
  
• PhD in Computer Science or closely related field
  
• Demonstrated record of publications in relevant computer science venues and/or general science journals
  
• Experience with training and evaluating generative AI models, working with large-scale messy datasets (e.g., mobility data, health data), and interdisciplinary research (e.g., computational social science, AI for health)
  
• 2-year postdoc is preferred, but we will consider applications for 1-year postdocs
  

• Curriculum Vitae - Your most recently updated C.V.

  
  

• Representative paper - Please include one research paper where you played a substantial role and that are representative of your work and interests.

  
  

• Second Representative paper - Please include one research paper where you played a substantial role and that is representative of your work and interests.

  
  

• Statement of Research - (max 4 pages). Please discuss your past research and accomplishments (e.g., publications, awards, presentations, evidence of real-world impact). Please also describe your plans for future research, especially how they would align with this position.

  
  

• Mentorship statement - (max 1 page). Please describe your past experience with mentoring junior students, including PhD students, master's, or undergraduate, and your philosophy towards mentorship. This could include mentoring students on research projects and/or teaching experience.

  
  

• 3 required (contact information only)

Help contact:

UC Berkeley is committed to diversity, equity, inclusion, and belonging in our public mission of research, teaching, and service, consistent with UC Regents Policy 4400 and University of California Academic Personnel policy (APM 210 1-d). These values are embedded in our Principles of Community, which reflect our passion for critical inquiry, debate, discovery and innovation, and our deep commitment to contributing to a better world. Every member of the UC Berkeley community has a role in sustaining a safe, caring and humane environment in which these values can thrive.

The University of California, Berkeley is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.

For more information, please refer to the University of California's Affirmative Action and Nondiscrimination in Employment Policy and the University of California's Anti-Discrimination Policy.

In searches when letters of reference are required all letters will be treated as confidential per University of California policy and California state law. Please refer potential referees, including when letters are provided via a third party (i.e., dossier service or career center), to the UC Berkeley statement of confidentiality prior to submitting their letter.

As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements.

Unless stated otherwise, unambiguously, in the position description, this position does not include sponsorship of a new consular H-1B visa petition that would require payment of the $100,000 supplemental fee.

As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct.

• 
  
• "Misconduct" means any violation of the policies or laws governing conduct at the applicant's previous place of employment, including, but not limited to, violations of policies or laws prohibiting sexual harassment, sexual assault, or other forms of harassment or discrimination, as defined by the employer.
  
• UC Sexual Violence and Sexual Harassment Policy
  
• UC Anti-Discrimination Policy
  
• APM - 035: Affirmative Action and Nondiscrimination in Employment
  

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• 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
  

*At Securian Financial the internal position title is Data Science Sr Analyst or Data Science Consultant. The title and salary will be determined based on experience and applied skills.*

Summary

As an Operational Support Data Scientist at Securian Financial, you will bridge advanced analytics and day-to-day business operations by designing, deploying, monitoring, and continuously improving AI-driven solutions that support enterprise processes.

This role focuses on supporting reliable, scalable, and explainable AI solutions that enhance operational efficiency, decision support, customer experience, and risk management across Digital, Marketing, Sales, and Servicing functions.

You will operate at the intersection of data science, MLOps, and the business - ensuring models are maintained, enhanced, monitored, and aligned with Securian's Enterprise Data Strategy Vision and Operating Principles.

Responsibilities include but are not limited to:

AI Solution Development & Deployment

• Work with business teams to enhance existing solutions to enhance and optimize existing AI/ML solutions.

• Deploy and manage solutions using cloud-native tools (e.g., AWS SageMaker).

Operational Model Support & Optimization

• Monitor model performance, data drift, and operational KPIs.

• Troubleshoot production issues and continuously enhance and optimize models for performance, stability, and cost efficiency.

• Establish measurement frameworks to quantify operational impact of deployed solutions.

Data Engineering & Analytical Execution

• Transform structured, semi-structured, and unstructured data into actionable features and insights.

• Perform exploratory analysis and visualization to identify operational improvement opportunities.

• Collaborate with engineering teams to productionize data solutions.

Stakeholder Engagement & Explainability

• Partner with cross-functional operational stakeholders to understand business workflows and translate them into AI-enabled solutions.

• Communicate complex AI methodologies and results clearly to technical and non-technical audiences.

• Ensure model transparency, explainability, fairness, and ethical AI application in alignment with enterprise governance standards.

Required Qualifications

• Demonstrated experience developing, deploying, or supporting production AI/ML models in cloud environments.

• Strong proficiency in Python and experience with tools such as AWS SageMaker and GitHub.

• Experience building operationalized data science solutions (not just prototypes).

• Strong understanding of statistical modeling, machine learning algorithms, and model validation techniques.

• Ability to clearly explain technical concepts, model outputs, and operational trade-offs to stakeholders.

• Strong ethical judgment with a commitment to responsible and unbiased AI development.

Preferred Qualifications

• 2+ years of hands-on experience in data science, applied AI, or machine learning.

• Experience supporting AI solutions in operational or production environments.

• Familiarity with MLOps practices, model governance frameworks, and automation tooling.

• Experience working in regulated industries (financial services preferred).

#LI-hybrid **This position will be in a hybrid working arrangement.**

Securian Financial believes in hybrid work as an integral part of our culture. Associates get the benefit of working both virtually and in our offices. If you're in a commutable distance (90 minutes) you'll join us 3 days each week in our offices to collaborate and build relationships. Our policy allows flexibility for the reality of business and personal schedules.

The estimated base pay range for this job is:

Pay may vary depending on job-related factors and individual experience, skills, knowledge, etc. More information on base pay and incentive pay (if applicable) can be discussed with a member of the Securian Financial Talent Acquisition team.

Be you. With us. At Securian Financial, we understand that attracting top talent means offering more than just a job - it means providing a rewarding and fulfilling career. As a valued member of our high-performing team, we want you to connect with your work, your relationships and your community. Enjoy our comprehensive range of benefits designed to enhance your professional growth, well-being and work-life balance, including the advantages listed here:

Paid time off:

• We want you to take time off for what matters most to you. Our PTO program provides flexibility for associates to take meaningful time away from work to relax, recharge and spend time doing what's important to them. And Securian Financial rewards associates for their service by providing additional PTO the longer you stay at Securian.

• Leave programs: Securian's flexible leave programs allow time off from work for parental leave, caregiver leave for family members, bereavement and military leave.

• Holidays: Securian provides nine company paid holidays.

Company-funded pension plan and a 401(k) retirement plan: Share in the success of our company. Securian's 401(k) company contribution is tied to our performance up to 10 percent of eligible earnings, with a target of 5 percent. The amount is based on company results compared to goals related to earnings, sales and service.

Health insurance: From the first day of employment, associates and their eligible family members - including spouses, domestic partners and children - are eligible for medical, dental and vision coverage.

Volunteer time: We know the importance of community. Through company-sponsored events, volunteer paid time off, a dollar-for-dollar matching gift program and more, we encourage you to support organizations important to you.

Associate Resource Groups: Build connections, be yourself and develop meaningful relationships at work through associate-led ARGs. Dedicated groups focus on a variety of interests and affinities, including:

• Mental Wellness and Disability

• Pride at Securian Financial

• Securian Young Professionals Network

• Securian Multicultural Network

• Securian Women and Allies Network

• Servicemember Associate Resource Group

For more information regarding Securian's benefits, please review our Benefits page.

This information is not intended to explain all the provisions of coverage available under these plans. In all cases, the plan document dictates coverage and provisions.

Securian Financial Group, Inc. does not discriminate based on race, color, religion, national origin, sex, gender, gender identity, sexual orientation, age, marital or familial status, pregnancy, disability, genetic information, political affiliation, veteran status, status in regard to public assistance or any other protected status. If you are a job seeker with a disability and require an accommodation to apply for one of our jobs, please contact us by email at , by telephone (voice), or 711 (Relay/TTY).

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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.

Job Title: Data Architect with AI experience

Location: Hybrid – Charlotte, NC

Job Summary

Platform AI Architect responsible for implementing and scaling AI platforms within the organization, driving AI adoption, improving productivity through AI agents, and establishing best practices for enterprise AI usage.

Required Skills

• AWS
• Python
• Snowflake
• AI / Artificial Intelligence
• SQL

Key Responsibilities

• Lead rollout of Nucleus AI platform and establish best practices and governance.
• Oversee AI onboarding and rollout across teams to ensure adoption.
• Help teams achieve ~40% productivity improvement through AI implementation.
• Design and develop AI agents for automation and productivity enhancement.

Required Skills & Qualifications

• 5+ years experience as a Solution Architect or Data Architect with AI experience
• Strong expertise in: SQL/Snowflake/Python, AWS experience.
• Strong communication and coordination skills
• Experience working in enterprise environments
• Ability to drive cross-functional projects
• Overall 10+ years of total IT experience

Job Title: Software Product Manager

Reports to: Chief Product Officer

Location: Westminster, CO (Hybrid — 3-4 days on site)

Compensation: Salary range $135k-$165k

About Inovonics

Inovonics builds enterprise grade wireless sensor networks and software for life safety and security systems in demanding environments. For over 40 years, our 900 MHz wireless technology has helped protect people, property, and critical operations across senior living communities, healthcare campuses, schools, retail sites, and other commercial facilities. We have shipped more than 25 million devices worldwide.

We are in the middle of a deliberate transformation: moving from a hardware component provider to a complete solutions company. That means building a software platform, cloud integrations, and AI-driven capabilities alongside our wireless hardware. This role sits at the center of that shift. Inovonics is a wholly owned subsidiary of Roper Technologies (NYSE: ROP).

The Role

We are looking for a Software Product Manager who can guide our software engineering team with clarity and technical credibility. This is not a role for someone who will hand requirements over the wall and hope for the best. You will be embedded in the day-to-day work of the team: running sprint ceremonies, owning the backlog, writing requirements that engineers can act on, and making product decisions that hold up under scrutiny.

The right candidate has a software engineering or technical background and has moved into product management because they want to shape what gets built, not just how it gets built. You understand system architecture well enough to know when a proposal does not fit, and you are willing to say so. You write requirements you have thoroughly reviewed and own the outcomes that follow.

What You Will Own

Day to day software team leadership

• Own the product backlog and sprint planning for the software engineering team
• Write and maintain requirements that are accurate, scoped, and actionable
• Run Agile ceremonies and serve as the primary product voice to engineering
• Manage trade-off decisions between scope, quality, and timeline in real time
• Report sprint progress to executive leadership
• Work with customer-facing teams and market focused Product Managers to triage requests and manage communication related to software releases

Software roadmap and platform strategy

• Define the “What”, “Why”, and “When” of what the software team builds.
• Own the software and cloud platform roadmap, including API integrations and third-party partnerships
• Ensure roadmap initiatives have well defined software requirements before engineering picks them up
• Partner with hardware and market focused product managers to identify what software can realistically deliver, surface revenue opportunities, and align software commitments to system level plans.
• Maintain visibility into technical dependencies and surface risks early

AI product direction

• Provide product leadership for Inovonics' AI capabilities, including our dealer facing AI assistant and AI driven monitoring features
• Translate the engineering team's AI and ML capabilities into a committed, realistic roadmap
• Identify where AI creates genuine product value versus where it adds complexity without payoff
• Work with commercial and customer facing teams to validate AI use cases before committing engineering resources

What Success Looks Like in Year One

• The engineering team trusts your requirements and does not spend cycles reworking stories because of gaps or inaccuracies
• The software backlog is clean, prioritized, and current
• Roadmap projects have software requirements captured and ready before they enter sprint planning
• A clear, defensible AI roadmap exists with prioritized use cases tied to business outcomes
• You are operating independently and are the recognized product leader for the software team

What We Are Looking For

Required

• Background in software engineering, computer science, or a related technical discipline
• Hands on experience running Agile development: sprint planning, backlog management, story writing, and retrospectives
• Demonstrated ability to write detailed, accurate product requirements that engineering teams can execute without constant clarification
• Ability to engage in architectural conversations and recognize when a proposal does not fit the existing system
• Experience owning a SaaS or cloud platform product end-to-end
• Comfort with ambiguity and the ability to make and defend product decisions with incomplete information

Strongly preferred

• 5+ years of product management experience with a software or engineering foundation
• Experience with IoT, connected hardware, or embedded systems products
• Familiarity with physical security, life safety, or building automation markets
• Experience shipping AI or ML powered product features; practical understanding of what AI systems can and cannot do
• Experience with API platform products and third-party integration ecosystems

Personal qualities that matter for this role

• You own your outputs. If you put a requirement in front of engineering, you have read it, pressure tested it, and stand behind it
• You know what you do not know and say so
• You earn credibility through rigor, not title
• You can move between strategic and operational work in the same day without losing focus

Location and Work Model

This position is based in Westminster, Colorado. We work in a hybrid model with 3-4 days per week on site. Westminster sits within the Denver/Boulder tech corridor, approximately 15 miles northwest of Denver.

Inovonics values diversity of thought and background and provides equal employment opportunity to all qualified applicants without regard to race, color, religion, national origin, gender, sexual orientation, age, veteran status, or disability.