Top AI Jobs in San Francisco Bay Area, CA

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.

Designs and deploys AI solutions with enterprise clients, from workflow discovery and prototypes through production tooling. Responsibilities include interviewing stakeholders, defining specifications and success metrics, building and testing LLM-based tools, designing interfaces, communicating with executives, leading change management, and driving adoption. Requires Python or SQL proficiency, executive communication, client-facing delivery experience, on-site work in San Francisco, and 20–40% travel to client sites.

Lead end-to-end performance of customer-facing conversational AI and knowledge base. Design, test, and optimize AI conversations, define intents and success metrics, analyze KPIs, maintain enterprise knowledge, run continuous improvement using analytics and experimentation, and partner cross-functionally to improve CX and business outcomes.

Own cross-functional programs for scaling and evolving AI training infrastructure across pre-training, mid-training, and post-training. Define the training-framework roadmap, coordinate research and infrastructure engineering teams, identify bottlenecks, improve training-run visibility, establish durable operational processes, and translate technical complexity into executive decisions. The role requires deep understanding of distributed training, GPU clusters, schedulers, networking, storage, and large-scale infrastructure operations.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Leads Mastercard’s global AI Engineering organization, overseeing enterprise AI products, agentic AI solutions, reusable AI capabilities, and production deployments. Responsibilities include translating business needs into secure, scalable AI solutions; ensuring reliability, governance, compliance, privacy, and operational excellence; driving adoption and measurable outcomes; and partnering across technology, security, risk, product, and business teams. The role also develops global engineering leaders and builds a high-performing organization.

Leads strategy, roadmap, investment, and execution for horizontal AI capabilities, shared AI platforms, and AI-native cybersecurity products. Translates AI research into reliable, secure, scalable customer solutions; drives adoption across product teams; establishes launch and autonomy criteria; partners with customers and cross-functional leaders on commercialization; and builds a high-performing AI product organization.

Lead the design, development, and operation of SoFi’s AI developer experience platform, including AI Gateway, model routing, evaluation systems, harness vending, and cost telemetry. Establish engineering standards, guide architecture and cross-team execution, improve CI/CD and operational reliability, mentor engineers, and partner with internal developers to drive adoption of AI coding tools. Ensure secure, compliant use of AI systems and maintain the platform’s technical vision.

Leads architecture and technical strategy for enterprise AI developer experience, including AI gateways, model routing, evaluation platforms, harness vending, and AI economics. Drives cross-team initiatives, resolves distributed-system bottlenecks, establishes engineering standards, leads incident response, evaluates AI tooling, and mentors senior and staff engineers. Requires hands-on experience with LLM architectures, model APIs, AI-assisted development tools, evaluation pipelines, dynamic routing, platform infrastructure, and infrastructure economics.

Lead the Core Product engineering organization building Albert OS, an AI-native chemistry platform. Own the full application layer, including front end, back end, APIs, ontology, and scientific context graph. Lead 8–10 engineers, hire and develop technical leaders, establish agentic development practices, and scale complex production systems. Partner with AI, product, infrastructure, and security teams to embed agentic experiences across scientific workflows while remaining hands-on with prototyping and shipping.

Conduct research on AI evaluation, rubrics, benchmarks, human judgment, preference data, and agent performance. Design rigorous experiments, validate measurement frameworks, build evaluation pipelines, and create long-horizon real-user benchmarks. Collaborate with engineers, delivery teams, customers, academics, and external labs, while publishing methods and benchmark findings. The role combines applied statistics, messy human data, research prototyping, technical writing, and deployment of research methods in live customer programs.

Build and deploy scalable, responsible AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architectures, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to platform roadmaps, and mentor engineering staff.

Design, build, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor engineering teams.

Leads product strategy and operations for AI/ML platforms, enabling associates to build, train, deploy, and scale models and generative AI applications. Defines roadmaps, prioritization, operational frameworks, and strategic planning processes while advocating for data scientists and engineers. Manages teams, partners with Product, Engineering, TPM, and business functions, oversees concurrent programs, and applies analytics to product, marketing, and credit risk decisions.

Develop and validate clinical heuristics and algorithms using real-world healthcare data. Build Python profiling pipelines, automated data-quality checks, and longitudinal patient cohorts. Analyze complex clinical databases and ETL pipelines, evaluate AI, NLP, and heuristic outputs, and collaborate with clinical, labeling, engineering, and data science teams. Maintain documentation, configuration, metrics, and validation standards for transparent, high-quality clinical analytics.

Leads Salesforce’s Forward Deployed Engineering team in delivering enterprise Agentic AI solutions. Defines deployment strategy, multi-agent architectures, development lifecycle standards, governance, and operating models. Builds and develops a high-performing technical team, partners with Product, Engineering, Sales, Security, and Legal, and influences Agentforce’s product roadmap. Requires deep expertise in enterprise AI, LLMs, RAG, distributed systems, orchestration, and secure production deployments, with 30% customer travel.

Lead the vision, strategy, and roadmap for AI experiences across Atlassian’s core products. Partner with engineering, design, data science, platform, and product teams to deliver intuitive AI features, accelerate adoption, define success metrics, gather customer feedback, and iterate based on data. Serve as an AI product leader, mentor stakeholders, communicate with executives, and drive innovation across a global software portfolio.

Leads enterprise AI engineering strategy and multi-team execution. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and production optimization. Makes technology decisions, scales reusable AI infrastructure and responsible AI practices, partners with research and risk teams, and develops engineering talent. Requires strong hands-on expertise in AI systems, cloud deployment, model optimization, and organizational leadership.

Build and deploy production AI/ML systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, cost, lead-time, supplier-risk, anomaly-detection, and cash-flow modeling; real-time inference pipelines; MLOps; data infrastructure; governance; and cross-functional AI adoption. The role also sets the supply-chain AI roadmap, mentors junior scientists, establishes modeling standards, and partners with Planning, Procurement, Finance, Engineering, and Operations.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Leads the Safety AI organization and architecture for on-device and cloud-based computer vision and machine learning systems. Oversees sensor fusion, model lifecycle management, platform architecture, experimentation, deployment, monitoring, and continuous improvement across more than two million dashcams. Partners with product, firmware, full-stack, and platform teams to deliver reliable, low-latency safety features. Builds and develops teams of applied scientists and engineers while driving measurable improvements in driver safety and customer outcomes.

Build and deploy generative AI solutions, including APIs, MCP servers, AI agents, LLM integrations, and full-stack applications. Develop scalable AWS cloud services, observability systems, evaluation pipelines, and production agent workflows. Collaborate with Product, UX, Security, Legal, and engineering teams to design interfaces, adapt AI tools to company codebases, and support customer experience and developer productivity use cases.

Build and operate production AI systems for FDA regulatory intelligence, including evaluation pipelines, document ingestion and retrieval, multi-agent workflows, data pipelines, cloud infrastructure, and secure multi-tenant services. Collaborate with former FDA experts to translate domain judgment into product requirements and evaluation cases. Own architecture, reliability, observability, cost, security, and rapid 0-to-1 delivery across the stack.

Research, design, train, and deploy multimodal AI models using clinical and molecular data to support precision medicine, biomarker discovery, therapy outcome prediction, and companion diagnostics. The role requires collaboration with life sciences companies, regulatory alignment for healthcare products, and publication of original research. Responsibilities include working with pathology slides, genomic profiles, spatial omics, and longitudinal patient records while contributing hands-on model development and product-level engineering.

Build and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Own complex architectures, reliability SLOs, security and compliance reviews, technical roadmaps, and cross-functional delivery while mentoring engineering teams.