AI Jobs in San Francisco

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Contributors may design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; and apply structured scoring criteria to assess multi-step problem solving.

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As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies, oversee the end-to-end health of the platform ensuring seamless integration between AI core and full-stack components, build automated systems to monitor model performance and data drift across geographically dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead response for production issues in mission-critical environments ensuring rapid resolution and prevention, translate technical performance metrics into clear insights for senior international government officials, and partner with Engineering and ML teams to ensure field lessons influence future technical architecture and decisions.

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Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment. Develop and implement state of the art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes, and agentic workflows. Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. Forge and manage relationships with customers' leadership and stakeholders to ensure successful development and deployment of AI projects with Snorkel Flow. Collaborate closely with pre-sales Solutions and Product teams to map customer needs to existing capabilities, prioritize roadmap gaps, and guide successful project setup. Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices. Lead stakeholder education on quantitative capabilities, helping them to understand the strengths and weaknesses of different approaches and what problems are best-suited for Snorkel AI. Serve as the voice of customers for new AI paradigms, data science workflows, and share customer feedback to product teams. Conduct one-to-few and one-to-many enablement workshops to transfer knowledge to customers considering or already using Snorkel AI. Travel up to 25% annually.

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Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment, leveraging Snorkel Flow or designing custom approaches. Develop and implement state-of-the-art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes, and agentic workflows. Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. Manage relationships with customers' leadership and stakeholders to ensure successful development and deployment of AI projects with Snorkel Flow. Collaborate closely with pre-sales Solutions and Product teams to map customer needs to existing capabilities, prioritize roadmap gaps, and guide successful project setup. Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices. Lead stakeholder education on quantitative capabilities, helping them understand the strengths and weaknesses of different approaches and suitable problems for Snorkel AI. Serve as the voice of customers for new AI paradigms and data science workflows, sharing customer feedback with product teams. Conduct enablement workshops to transfer knowledge to customers considering or using Snorkel AI. Travel up to 25% annually.

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Develop, test, and deploy production-level code across backend and AI systems. Collaborate with AI researchers to integrate and optimize large language models for insurance workflows. Build data processing and evaluation pipelines for unstructured document inputs such as PDFs, emails, and images. Contribute to core infrastructure including APIs and orchestration logic powering the AI Workspace for Insurance. Work cross-functionally with product and customer teams to identify and solve real business problems using AI. Participate in design reviews, code reviews, and rapid iteration cycles.

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The Insurance Product Manager is responsible for owning the full lifecycle of AI extraction workflows on the platform, including scoping, architecture, prototyping, evaluation, and iteration. They will design and build complex insurance workflows such as submission intake, policy comparison, underwriting audits, and claims workflows into structured, testable AI workflows from scratch. The role includes defining ground truths and evaluation sets to measure accuracy and quality, running continuous benchmarks, and identifying quality gaps before customers do. They will work directly with customers and Forward Deployed Engineers to configure, test, and iterate workflows toward production, bringing insurance process expertise and technical judgment to every deployment. The Insurance Product Manager acts as the bridge between domain expertise and engineering teams by translating insurance needs into technical solutions.

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As a Forward Deployed Engineer Strategist, you will work with Engineers, Product Designers, and other Strategists to deploy voice AI technology to solve customer problems. Responsibilities include meeting with strategic customers to understand their audio and voice AI needs, identifying relevant use cases through engagement with customer problems, designing and architecting custom integrations, guiding customers on best practices for AI model implementation, presenting results and proposals to technical and executive audiences, collaborating with Research and Product teams to incorporate field insights, building and delivering demos of AI technology, scoping potential applications in new industries, taking full ownership of major projects for strategic partners, and working daily with customers' engineering and executive teams to ensure optimal implementation of ElevenLabs' technologies.

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Design rigorous statistics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn); improve AI reasoning to align with industry-standard logic; apply structured scoring criteria to multi-step problems.

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Contributors may design rigorous statistics problems reflecting professional practice, evaluate AI solutions for correctness, assumptions, and constraints, validate calculations or simulations using Python libraries such as NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn, improve AI reasoning to align with industry-standard logic, and apply structured scoring criteria to multi-step problems.

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Contributors may design rigorous statistics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn); improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.