Top AI Jobs in San Francisco Bay Area, CA

Analyze large datasets using quantitative methods, data mining, statistical modeling, and AI tools to generate actionable product and business insights. The role designs and tracks product metrics, builds dashboards and reports, identifies trends and opportunities, and supports data-driven decisions. Responsibilities include collaborating with product managers, engineers, marketers, and designers, applying statistical techniques such as A/B testing and causal analysis, and independently driving projects to completion.

Leads software engineering and AI platform initiatives, designing secure, scalable cloud infrastructure for AI/ML workloads. Builds Kubernetes-based platforms, CI/CD pipelines, automation, and infrastructure-as-code for ML deployment and operations. Partners with AI teams on infrastructure requirements, optimizes cloud performance and costs, and promotes responsible AI-assisted engineering practices, governance, validation, and secure SDLC automation.

Own the centralized MCP gateway and AI tool-access platform, including connector onboarding, identity and access controls, provisioning workflows, security guardrails, operations, monitoring, governance, adoption metrics, and technical roadmap. Partner with security, IAM, engineering, and business leaders while mentoring engineers and analysts. Automate access governance and support regulated financial-services requirements.

Conduct foundational and applied ML research on AI well-being and moral status, design and run experiments (including tooling/infrastructure), collaborate with external researchers, and publish papers, blog posts, and talks to inform AI developers, policymakers, and the public.

Lead enterprise AI engineering strategy and teams building scalable agentic AI platforms. Oversee foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and AI operations. Drive build-versus-buy decisions, optimization, responsible AI practices, multi-year technical roadmaps, and cross-functional alignment while developing senior engineering talent and supporting hundreds of production use cases.

Design, develop, deploy, and support foundational AI systems, including large language model inference, agents, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, scalability, and hardware utilization. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor senior technical staff.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, and observability. Lead multi-model orchestration, cost-performance governance, architecture reviews, technical roadmaps, and responsible AI standards. Mentor senior technical staff and collaborate with engineers, scientists, product managers, and program managers to deliver scalable AI products.

Drive customer adoption of BrazeAI by leading workshops, diagnostics, and accelerators from AI use-case ideation through launch and impact measurement. Advise technical and marketing stakeholders, identify adoption barriers, develop AI playbooks and prototypes, recommend services partners, and provide AI thought leadership. The role combines customer-facing consulting, marketing technology expertise, applied AI knowledge, and cross-functional collaboration, with travel up to 25% at peak times.

Build and operate SoFi’s internal AI platform through production-grade microservices, telemetry pipelines, AI integrations, and developer tooling. Own feature delivery, architecture, testing, deployment, incident response, and operational excellence. Collaborate with partner teams and internal customers, improve platform adoption, participate in design reviews, and mentor engineers. Apply AI-assisted development responsibly while protecting sensitive data and maintaining rigorous security, testing, and governance standards.

Build and deploy agentic and generative AI copilot and autopilot systems for risk and compliance automation. Own end-to-end ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based model development. Collaborate with product, engineering, platform, and operations teams to improve fraud detection and analyst productivity. Drive strategic roadmaps and cross-functional programs with significant business impact.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Lead the Prism engineering team building SoFi’s internal AI productivity platform. Manage and develop software engineers, oversee AI infrastructure such as model harnesses, routing, and evaluations, define the technical roadmap, and partner with internal developer customers to drive platform adoption. The role requires people management, AI developer-tooling expertise, platform engineering experience, and the ability to deliver rapidly in an ambiguous, greenfield environment.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization; define reliability objectives; own complex system architecture; lead technical reviews; ensure security and compliance; and mentor engineering associates.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for cost, latency, throughput, and hardware utilization. Architect multi-model orchestration pipelines, establish AI engineering standards, lead technical reviews, and mentor senior AI engineers.

Lead strategic customers and partners in designing, implementing, and optimizing scalable cloud architectures for AI/ML and GPU workloads. Serve as a trusted technical advisor, conduct workshops and proofs of concept, modernize workloads, deploy and fine-tune LLMs, and accelerate cloud adoption. Collaborate with sales, engineering, product, and operations teams to solve complex challenges, provide product feedback, and support account expansion, retention, and long-term technical success.

Own the architecture and technical strategy for Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations while establishing AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering leaders to translate business needs into scalable solutions, make build-versus-buy decisions, mentor engineers, and improve business impact through data and user feedback.

Build and ship production backend services and AI-powered voice agent features. Own customer problems end to end, develop reusable agent modules, improve menu and catalog accuracy, contribute to model fine-tuning, run experiments, and integrate solutions into production. The role involves close customer collaboration, rapid experimentation, and working across Kotlin, Python, and TypeScript in an in-person San Francisco environment.

Set organization-wide AI architecture direction, establish evaluation and production-readiness practices, guide teams from prototypes to production, and build reusable patterns for reliable, secure, observable AI systems. Partner with product and engineering leaders to connect AI initiatives to measurable workflow outcomes. Prototype high-risk designs, review implementations, evaluate emerging models and agent frameworks, and advise on trade-offs among quality, autonomy, latency, cost, safety, and complexity.

Leads an AI engineering team developing scalable generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and agentic workflows. Sets technical vision, makes build-versus-buy decisions, optimizes production AI systems, operationalizes Responsible AI standards, develops team roadmaps, collaborates cross-functionally, and mentors engineering talent.

Lead AI evaluation research by defining quality criteria, designing human and automated studies, building rubrics, datasets, benchmarks, and thresholds, and analyzing model performance. Validate automated graders and LLM-as-judge methods, identify failure patterns and uncertainty, and translate findings into model, product, and release decisions. Collaborate with AI researchers, engineers, and cross-functional stakeholders while developing reusable evaluation frameworks and mentoring peers.

Build Superhuman Mail’s AI platform, including MCP tools and standards, agentic workflows, personalization, LLM evaluation systems, and model-selection strategies. The role involves shipping production LLM features, partnering across teams on reusable AI capabilities, and potentially shaping data collection and modeling approaches. Engineers will work primarily with backend systems and applied AI while adapting quickly to evolving technologies and product priorities.

Own the AI research roadmap from problem identification through production deployment. Develop LLM and deep learning methods for legal text reasoning, extraction, drafting, redlining, hallucination mitigation, explainability, and efficient inference. Build evaluation datasets and benchmarks, assess emerging techniques, collaborate with Engineering and Product, and communicate findings to technical and non-technical audiences.

Conduct applied AI research for legal technology, focusing on LLMs, deep learning, long-context reasoning, contract analysis, information extraction, hallucination mitigation, explainability, and model evaluation. Own research roadmaps, design experiments, build benchmarks, develop prototypes, and ship successful approaches into production with Engineering and Product. Communicate findings to technical and non-technical stakeholders.

Applies AI and machine learning techniques to product specifications, recommendations, and agentic workflows. Responsibilities include prototyping, evaluating, and iterating on models and AI-powered customer features; analyzing structured and unstructured data; translating customer problems into technical approaches; and documenting results. The intern collaborates with Applied AI Engineers, Product Managers, and Engineering while gaining experience shipping practical AI systems.

Conducts research on how frontier AI affects constitutional democratic institutions and the rule of law. Responsibilities include developing legal-alignment AI safety evaluations, analyzing institutional vulnerabilities, examining novel legal questions involving liability and agency, and identifying applications that strengthen civic, legislative, and judicial processes. The role requires rigorous public-facing scholarship or policy analysis, interdisciplinary expertise spanning AI and law or political science, and significant autonomy in high-stakes research.