Model Policy Manager in San Francisco

About Snorkel At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! About This Role We're looking for a Staff HR Business Partner to build and own the people strategy for Snorkel's Data as a Service (DaaS) organization. This role is hybrid ( 3 days/week in office) in San Francisco, CA . The DaaS org is a delivery-first team that has more than tripled in size over the last six months, with no signs of slowing. They deliver high-quality data operations and AI deployment outcomes for frontier labs and AI teams. This org has a unique composition: forward deployed engineers, technical and operations delivery managers, a supply team managing a workforce comprised of multiple worker types at scale, and others. The people challenges here require an HRBP who has seen this kind of complexity before, such as workforce planning across FTEs and contractors, building a high performance culture rooted in delivery outcomes, and keeping a geographically dispersed, operationally complex team connected to Snorkel's culture. You'll partner directly with our DaaS GM and leadership team, and you'll need to be as comfortable in the operational weeds as you are in strategic conversations. The ideal background is professional services, managed services

About the Team The Finance & Strategy team accelerates the growth of our business by identifying and implementing new opportunities and solutions. We partner with teams across the company to improve the profitability, scalability and defensibility of our business model. We're looking for people who can use strategy to identify areas of opportunity, and collaborate to help carry projects from conception through execution. About the Role We are looking for a Finance Manager who will work with partners across DoorDash to ensure we are making the most efficient investment and capital allocation decisions while improving the experience for our merchants and other marketplace participants. You will help lead financial reporting, forecasting, and planning, and will guide financial insights and direction for all of our operational decisions and strategies. You’re excited about this opportunity because you will… Conduct weekly reporting of financial performance of DoorDash’s Merchant business, forecast key financial metrics based on quantitative analysis of historical data.Support quarterly financial planning, collaborate with cross functional business partners to identify growth areas and business priorities.Deep-dive into complex operational questions and collaborate cross-functionally outside Finance (Sales, Operations, Support) as well as within Finance (Accounting, Analytics, BI) to uncover the most optimal data-driven business decisions and investments.Identify key opportunities & risks to the business, quantify and evaluate the impacts, propose a solution and investment case, and ensure efficient execution. Leverage and apply knowledge of SQL, finance, analytics, and technology to manage financial reporting, forecasting, and quantitative analysis to assist DoorDash in making better business decisions. We’re excited about you because… 6+ years of experience overall, preferably from Strategic Finance, Corporate Finance, Investment Banking, or Consulti

Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including frontier model training, enterprise adoption, defense applications, and autonomous vehicles. Our mission is to develop reliable AI systems for the world's most important decisions. The next frontier for AI is the physical world. We're looking for an AI Product Manager to own the Robotics vertical within our Physical AI team. In this role, you'll own both the development of the data and training environments (the teleoperated demonstrations, real-world collections, simulated tasks, and annotation products that labs use to train and evaluate robot policies) and the "data as a product" strategy that powers them. You'll understand where physical AI is headed, decide what robot tasks and embodiments are worth collecting, how to source and structure the underlying data, and how to turn deep domain knowledge into a defensible product. The ideal candidate has lived inside robotics or physical AI research, and is able to pair that domain understanding with a sense for where current robot policies succeed and fail in real-world workflows. You'll translate that expertise into datasets, environments, and evaluation frameworks that teach robots to do real physical work, and you'll be the domain expert Scale's most important customers and their leading researchers turn to. A strong entrepreneurial & go-to-market mindset will be necessary. What You'll Do Own the Robotics AI roadmap & data strategy: Set product direction for the robotics training stack and the data strategy behind it — what data we collect, on which hardware and embodiments, and what we source internally vs. through our marketplace. Establish a vision for where physical AI is heading, driving execution across engineering, operations, and go-to-market teams. Build partnerships with research teams at frontier labs: Work directly with researchers at leading physical AI labs to understand where their robot p

Scale AI is seeking a highly skilled and motivated Software Engineer, Frontier AI Infrastructure to join our dynamic Public Sector Engineering team. As a part of this team, you will own the model inference layer - enabling state of the art models, debugging the latest AI tools, managing networking, debugging latency, and tracking pricing/usage metrics for AI models. You will lead technical discussions on the frontlines with cloud vendors and customers to deliver on critical contracts and to debug platform issues. You will also work upstream with Product to understand features before they break, moving us from "infra-only debugging" to proactive integration testing. You will: Design and implement secure scalable backend systems for Public Sector customers, leveraging Scale's modern and cloud-native AI infrastructure. Own services or systems and define their long-term health goals, while also improving the health of surrounding components Re-architect the stack to run in compliant or restrictive environments. This requires designing swappable components (auth, storage, logging) to meet government/security mandates without breaking the product. You will work with Product to build integration tests that catch issues early, shifting the focus from "infra-only debugging" to preventing failures upstream. Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions. Contribute to the platform roadmap and product strategy for Scale AI's Public Sector business, playing a key role in shaping the future direction of our offerings. Must have: At least an active secret clearance and the ability & willingness to up level to TS/SCI with CI Poly. This is a requirement and candidates will not be considered who do not hold at least a secret clearance Ideally you'd have: Full Stack Development: Proficiency in both front-end and back-end development, including experience with modern web develo

Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including frontier model training, enterprise adoption, defense applications, and autonomous vehicles. Our mission is to develop reliable AI systems for the world’s most important decisions. We’re looking for an AI Product Manager to own the Finance vertical within our Agents Data & Reinforcement Learning Environments team. In this role, you’ll own both the development of RL environments (the realistic, high-fidelity simulations of financial software and workflows that labs use to train and evaluate agents) and the “data as a product” strategy that powers them. You’ll understand where AI is being used in the Finance industry, decide what financial tasks are worth modeling, how to source and structure the underlying data, and how to turn deep domain knowledge into a defensible product. The ideal candidate has lived inside the Finance industry, and is able to pair that domain understanding with a sense for AI research and current agent capabilities in Finance workflows. You’ll translate that expertise into environments and datasets that teach AI agents to perform real financial work, and you’ll be the domain expert Scale’s most important customers and their leading researchers turn to. A strong entrepreneurial & go-to-market mindset will be necessary. What You’ll Do Own the Finance AI roadmap & data strategy: Set product direction for the Finance agents training stack and the data strategy behind it. Establish a vision for where AI is continuing to transform the Finance industry (including investment banking, private equity, public markets, corporate finance, FP&A, etc), driving execution across engineering, operations, and go-to-market teams. Build partnerships with research teams at frontier labs: Work directly with researchers at leading AI labs to understand where their Finance agentic capabilities fall short and shape new product lines and competit

About the Team The Finance & Strategy team accelerates the growth of our business by identifying and implementing new opportunities and solutions. We partner with teams across the company to improve the profitability, scalability and defensibility of our business model. We're looking for people who can use strategy to identify areas of opportunity, and collaborate to help carry projects from conception through execution. About the Role We are looking for someone who will help guide our global strategy. You will be a strategic advisor to important partners across DoorDash to ensure we are making the most efficient investment and capital allocation decisions across the geographies in which we operate while improving the experience for all of our marketplace participants. You will help lead financial reporting and planning, and will guide financial insights and direction for our operational decisions and strategies. This role reports to a Senior Director in our Finance & Strategy organization and is remote-friendly within the United States. You’re excited about this opportunity because you will… Partner with senior leadership to help guide and run our Digital Ordering business, implementing, measuring and optimizing the SaaS and transactional components of the business Own the global financial performance picture for this business line and understand the levers to optimize our approach to balancing global growth vs. profitability Track and analyze key business/financial KPIs, including SaaS specific metrics, uncover trends and assess current/future business risks and opportunities Support DoorDash’s quarterly financial planning, collaborate with cross functional business partners to identify growth areas and business priorities. Leverage and apply knowledge of SQL, finance, analytics, and AI technology to manage financial reporting, forecasting, and quantitative analysis to assist DoorDash in making better business decisions. Deep-dive into complex operat

Revivn is a profitable and rapidly growing company that helps enterprises manage their technology through our end of life software platform. We take electronic recycling one step further by repurposing hardware that still has remaining life and providing it to people who lack dedicated computer access and make it more affordable for people who may not be able to purchase new technology. Working with companies like Instacart, Lyft, Qualtrics, X, Gensler, and Spotify, we are changing the way companies view used technology with a new model that focuses on repurposing instead of recycling. This role is based in the San Francisco Bay Area. You will operate independently in-market, meeting customers and prospects in person across the Bay Area and traveling as needed. Revivn combines the learning and growth potential of a startup with the stability of a proven business. We have been profitable since inception for more than 10 years while revolutionizing how enterprises dispose of outdated IT equipment. We’re looking for an Enterprise Account Executive to own the sales cycle for our most strategic enterprise opportunities, from first conversation to closed-won. This is a quota-carrying, high-ownership role for a seasoned closer with an established network in the Bay Area technology ecosystem. About the Role You’ll own a book of strategic enterprise accounts and turn targeted outreach into revenue. You’ll partner with Marketing on account strategy, run discovery and deal strategy with senior buyers, and collaborate with Account Management to set customers up for long-term success. You’ll report directly to the Co-CEO/Co-Founder. This role requires sound judgment, strong executive presence, and the ability to operate independently without a large local team around you. What You’ll Do Own the full enterprise sales cycle, from discovery and qualification through negotiation and close Build pipeline through your existing network, targeted outreach, and close partnership wit

AI only answers correctly when it can trust the data underneath it. This role owns two connected parts of how Sigma shows up in that world: where Sigma's experience lives outside its own product (MCP, a CLI, the Claude and ChatGPT marketplaces, integrations like Slack, Teams, and Glean), and the semantic layer that makes every one of those surfaces trustworthy. The first mandate is Sigma's AI ecosystem: defining Sigma's approach to MCP, giving external agents structured, governed access to Sigma's data model; owning the CLI, giving developers a fast way to work with Sigma outside the UI; and building Sigma's presence in the Claude and ChatGPT marketplaces, plus integrations for Slack, Teams, Glean, and similar surfaces, so people can reach Sigma's data wherever they already work. This is some of the most visible, fastest-growing surface area in the product. The second mandate is the semantic layer underneath it all. Every agent, chat answer, and integration is only as reliable as the data model behind it — get a metric definition wrong here, and every surface built on top inherits the mistake. This includes setting the roadmap for how semantic views connect across data platforms and how the model evolves as new AI capabilities emerge. What you'll do Define Sigma's approach to MCP, giving external agents and tools structured, governed access to Sigma's data model. Own the CLI roadmap, giving developers a fast, scriptable way to work with Sigma outside the UI. Build Sigma's presence in the Claude and ChatGPT marketplaces, along with integrations for Slack, Teams, Glean, and similar surfaces, so people can reach Sigma's data wherever they're already working. Set the roadmap for Sigma's data modeling strategy, including how semantic views connect across data platforms and how the semantic layer evolves as new AI capabilities emerge. Partner with engineering and design to ship integration and semantic modeling capabilities that hold up at enterprise scale.

About Scale AI At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. Scale Frontier Data is the organization behind the training and evaluation data that frontier labs depend on. We build the systems, tooling, and expert workflows that turn hard human expertise into signals that models can learn from, across reasoning, coding, agentic tool use, and domain expertise. Reinforcement learning environments are now the center of gravity for that work: the difference between a model that demos well and a model that reliably completes long-horizon work is almost always the quality of the environments and reward signals it was trained against. Responsibilities As a Staff Software Engineer, RL Environments, you'll own the technical foundation for how Scale builds, runs, verifies, and delivers RL environments at scale. An RL environment is a real piece of software: a containerized world with real dependencies, real state, real tools, and a grader that has to be correct even when the agent is creative about breaking it. Building one is a full-stack engineering problem. Building thousands of them reproducibly, cheaply, with trustworthy reward signals and throughput measured in millions of rollouts is a systems problem that very few people have solved. You'll work on both. You'll design the platform: sandboxed execution, environment packaging and versioning, rollout orchestration, trajectory capture, verifier frameworks, and the authoring surfaces that let engineers and domain experts produce environments without reinventing infrastructure each time. And you'll go deep on the environments themselves by instrumenting real applications, designing task suites that expose specific capability gaps, and building graders that

Scale works with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling). This role will focus on optimizing data curation and eval to enhance LLM capabilities in both text and multimodal modalities. In this role, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models. You will: Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities. Design and experiment new approaches to preference optimization. Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness. Publish research findings in top-tier AI conferences. Ideally you’d have: Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field. Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning. Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning. Excellent written and verbal communication skills Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals Previous experience in a customer facing role. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined du

Scale works with the industry's leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling) and evaluation. This role is on the evaluation pod within the GenAI Research Organization and will focus on building benchmarks and diagnosing model failure modes in both text and multimodal modalities. In this role, you will develop rigorous evaluations and diagnostic methods that reveal where frontier models fail and why. You will collaborate with researchers and engineers to define best practices in evaluation-driven AI development. You will also partner with top foundation model labs to translate failure analysis into technical and strategic input on the next generation of generative AI models. You will: Analyze model behavior to identify, characterize, and diagnose failure modes in frontier LLMs and Agents. You’ll identify everything from capability gaps and reasoning errors to robustness and alignment issues, all focusing on RCA. Design and build benchmarks and evaluation methods that measure LLM capabilities in both text and multimodal modalities. Apply post-training expertise (SFT, RLHF, reward modeling) to connect observed failures to the data and training interventions that address them. Publish research findings in top-tier AI conferences. Ideally you’d have: Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field. Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning. Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning, and with LLM evaluation or benchmark development. Excellent written and verbal communication skills. Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals. Previous experience in a customer facing r

About Scale AI At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI) and building upon our prior model evaluation work with enterprise customers and governments to deepen our capabilities and offerings for public and private evaluations. About Data Engine Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we produce is some of the most critical work for how humanity will interact with AI. About Our FDE Team Generating high-quality data is the core problem our business solves. We aim to make producing and delivering high-quality data seamless and efficient for operators and customers. Our Team is building customer and operator-specific infrastructure to provide high-quality data with low turnaround time. You'll be exposed to the cutting edge of the Generative AI industry while directly interfacing with the leading model-building organizations in the space, including the top AI research labs and government agencies. Join us in shaping the future of Artificial General Intelligence. As a Forward Deployed Engineer, you'll be at the forefront of providing the critical data infrastructure that powers the most advanced AI models, directly influencing how humanity interacts with AI. You will work with the world’s leading AI companies and government agencies to solve their most complex AI data-related problems. Responsibilities: Drive Impact: Directly contribute to the advancement of AI by delivering critical data solutions for leading AI innovators and

Director of Engineering, Physical AI Role Overview The Director of Engineering will report to the General Manager of Physical AI, and will be responsible for leading a multi-disciplinary engineering organization. In this senior leadership role, you will own the execution of the Physical AI Data Engine — the platform powering the next generation of Physical AI/Embodied AI. You will collaborate closely with Operations and GTM to guide product direction and help solve the data bottleneck that stands between today's robotics research and real-world deployment. This role requires significant ownership in a fast-paced environment and you will motivate internal teams to set the pace for business growth. Travel will come into play. Key Responsibilities: Set and drive the technical vision across data collection infrastructure, teleoperation systems, ML training pipelines, model evaluation frameworks, annotation tooling, and research Lead a multidisciplinary engineering organization—spanning engineering managers, software engineers, ML engineers, and ML research scientists—while designing the organizational structure, talent strategy, and culture required to scale rapidly without compromising on quality or strategic alignment Maintain exceptional technical and operational excellence by deeply understanding team deliverables, asking incisive questions, identifying slipping standards early, and knowing precisely when to step in Drive cross-functional alignment across Engineering, Operations, and GTM on platform architecture, release processes, and shared priorities Collaborate with researchers and clients to architect and deliver scalable, production-grade data infrastructure tailored for complex robotics workloads Required Qualifications: Bachelor's degree in Engineering, Robotics, Computer Science, or a related technical field 8+ years of engineering experience in fast-paced environments, including 4+ years direct people management demonstrated history of recruiting, mentorin

At Scale, we believe that AI will dramatically improve the world, and our mission is to accelerate the development of AI. We’re looking for a Field Marketing and Events leader to continue to build and manage Scale’s robust Gen AI and Research field marketing and events program, including but not limited to Scale hosted executive events, Scale hosted practitioner events and meetups, research conferences, and our annual flagship conference, AI Leadership Summit. You will join a rapidly growing team with the opportunity to manage and execute events from start to finish, drive lead generation and pipeline growth, and plan event programming with the largest names in AI. The successful candidate will have a solid understanding of technology, model builder, and research markets, strong project management skills, a strategic mindset, ability to manage and grow a team, and a passion for AI & technology. You will: Own annual planning for all Gen AI and Research events including strategy and budget Establish event activities in line with sales goals and deal acceleration, prioritizing goals from the go-to-market leadership and business development teams on event location and audience Manage team of field marketing and events managers who will execute on planning and logistics. This role should have a player-coach mentality and ability to also execute on nuts to bolts planning for all executive dinners, meetups, happy hours, sponsored trade shows, and hosted conferences Manage contractor relationships including event production firms and outside vendors, and event budgets Align with growth marketing on marketing campaigns and marketing qualified lead (MQL) reporting Track event campaign performance, measuring ROI, results, and metrics through Salesforce and own any course correction for events that are not performing Own all aspects of Scale hosted conference, Scale AI Base Camp, including venue selection, vendor management, logistics, speaker

About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations. About the ACE team The Agent Capabilities & Environments (ACE) team, part of Scale’s Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. About This Role This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate t