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.

Analyze large datasets using quantitative analysis, data mining, statistical modeling, metrics, dashboards, and reports to generate actionable product and business insights. Collaborate with product managers, engineers, marketers, and designers to improve product decisions and adoption. Develop scalable data solutions using AI tools while maintaining statistical rigor, and independently drive analytical projects to completion.

Leads data science for enterprise agentic AI products, developing and deploying machine learning models, designing experiments, analyzing telemetry, and translating findings into business strategy. Partners with data scientists, AI/ML engineers, and product managers while guiding large-scale analysis across numeric and textual data. The role also requires technical leadership, stakeholder communication, innovation, talent development, and expertise in statistical modeling, deep learning, and cloud-based data science.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Own the architecture and development of Klaviyo’s enterprise MarTech ecosystem, connecting marketing data, customer journeys, campaign execution, budgeting, and performance insights. Build AI-powered capabilities including generative AI, predictive scoring, and intent automation. Architect integrations across HubSpot, Salesforce, NetSuite, Coupa, and internal systems, advise on technical designs, support vendor evaluations, and partner cross-functionally to improve marketing efficiency and ROI.

Define and drive CrowdStrike’s company-wide AI and agentic architecture. Design production AI systems, shared platforms, lifecycle workflows, and developer tooling across products and business units. Improve performance, resilience, scalability, observability, governance, security, and auditability across cloud and edge environments. Build reference implementations, guide architectural decisions, resolve complex engineering challenges, mentor technical leaders, and align senior stakeholders around AI strategy and implementation.

Lead product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

Build and maintain knowledge and evaluation infrastructure for long-horizon AI agents. Develop production agents for complex customer use cases, analyze agent errors, and implement quality improvements. Build reliable backend systems supporting agent execution, evaluation, and monitoring, while partnering with senior engineers on platform reliability and quality. The role may also involve platform engineering, frontend work, and distributed systems.

Lead the AI Engineering function by advancing frontier AI capabilities, defining team strategy, hiring and mentoring AI engineers, supporting AI developer and research experiences, and delivering shared infrastructure such as search systems. The role collaborates closely with product and engineering teams and requires expertise in LLMs, transformer models, agentic workflows, context management, and AI evaluation.

Build AI-powered features and scalable agent infrastructure for Hex’s data analytics platform. Partner with research and product engineering teams on context, model prompting, experimentation tooling, and platform integration. Lead major product initiatives, develop performant web experiences, maintain high standards for testing and correctness, and mentor engineers. The role involves full-stack development across React, TypeScript, GraphQL, backend services, cloud infrastructure, and data systems.

Build and deploy AI capabilities for Hex’s Notebook Agent and context engine. Responsibilities include running experiments, fine-tuning models, developing AI infrastructure and experimentation tooling, architecting AI-enabled systems, and shipping production experiences for data scientists and analysts. The role partners closely with product teams, spans greenfield initiatives, and offers opportunities for technical and team leadership.

Lead the architecture, development, and production deployment of advanced AI systems for risk management and compliance. Design agentic frameworks, LangGraph workflows, distributed memory infrastructure, model-serving platforms, PEFT optimization, and AI observability solutions. Mentor engineers, establish technical strategy, collaborate across teams, and evaluate emerging GenAI, LLM, and NLP technologies. The role also requires integrating AI APIs and microservices with banking applications while ensuring scalability, reliability, compliance, and operational excellence.

Design, develop, deploy, and optimize production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead multi-model orchestration and cost-performance optimization across cloud infrastructure. Establish AI engineering standards, guide technical roadmaps, lead design reviews, and mentor senior AI engineers while integrating research advancements into scalable, responsible products.

Leads Coupa’s AI platform engineering strategy, roadmap, and global engineering organization across the US and India. Oversees model training, evaluation, serving, data infrastructure, AI use-case prioritization, strategic technology partnerships, hiring, budgets, and delivery. Ensures data privacy, tenant isolation, and responsible AI practices while presenting business impact and technical strategy to executive stakeholders.

Design and develop scalable software systems and production LLM solutions using RAG, agentic workflows, PEFT, and guardrails. Own architecture, technical execution, SDLC activities, cloud and Databricks governance, DevOps automation, testing, deployment, and system reliability. Collaborate with cross-functional teams, conduct code reviews, translate business requirements, and guide software design. The role is client-facing and follows a hybrid work model with travel as needed.

Develops and transfers AI research into real-world autonomous aircraft and vehicle systems. Evaluates multimodal perception models, builds prototypes, combines learned and classical computer vision methods, and optimizes capabilities for edge deployment. The role involves Python and C++ software, sensor fusion, model evaluation, robotics, and collaboration with researchers and autonomy engineers to deliver tested, documented production capabilities.

Design, build, and ship production LLM-powered features for Gusto Retirement. Implement and integrate LLMs (RAG, agents, prompting, tool loops), make pragmatic architecture decisions, collaborate with product, design, and engineering, write reliable well-tested code, and drive end-to-end execution and iteration.

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.

Design, develop, deploy, and support enterprise AI systems, including foundation models, LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Establish AI architecture standards, optimize model performance, cost, latency, and scalability, and lead model routing and orchestration initiatives. Drive responsible AI practices, contribute to technical roadmaps, mentor engineering teams, and influence cross-functional stakeholders and enterprise AI strategy.

Develop and deploy AI capabilities across enterprise ERP platforms including Workday and SAP Ariba. Build intelligent agents, chatbots, automation, integrations, and custom GenAI/LLM solutions. Establish PaaS environments, cloud services, deployment pipelines, security, governance, and engineering processes. Lead AI projects from concept through production, evaluate vendor versus custom solutions, support technology roadmaps, and promote AI literacy. Provide production support and training while aligning solutions with business needs and measurable ROI.

Supports AI governance projects by assessing risks and controls, documenting processes, gathering requirements, analyzing information, and synthesizing findings. Advises clients on regulatory compliance, risk management, internal controls, and governance improvements. The role involves applying AI/ML governance frameworks such as NIST and ISO, translating technical concepts for stakeholders, collaborating with diverse teams, and developing expertise in corporate governance and risk management.

Lead AI governance engagements by developing governance, risk, and control frameworks; analyzing regulations; managing client projects; mentoring staff; assessing AI/ML lifecycle risks; and advising stakeholders on compliant, transparent, and responsible technology adoption.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads enterprise AI governance initiatives, including governance frameworks, risk and compliance programs, controls, regulatory assessments, and responsible AI practices. Advises executive clients, oversees complex engagements, drives business development, develops innovative solutions, mentors teams, and translates technical AI governance concepts into actionable strategies. Requires expertise in AI/ML governance, risk, controls, audit, testing, validation, AI lifecycles, and leading frameworks such as NIST and ISO.