Top AI Jobs in San Francisco Bay Area, CA

Summer 2027 PhD research internship at Atlassian focused on AI research. The role supports distributed-first collaboration, with virtual interviews and flexibility to work remotely or from a company office.

Conduct AI research as a 2027 graduate hire at Atlassian, collaborating remotely or from company offices. The provided description contains no specific research area, responsibilities, qualifications, or technical requirements.

Lead and grow Cloudflare’s models team responsible for model integrations, launches, catalog accuracy, pricing, reliability, evaluations, rankings, auto-routing, and provider coordination. Build automation and agent workflows, monitor model health, detect regressions, and turn Gateway traffic data into trusted recommendations. The role combines people management, technical execution, product judgment, operational scalability, and hands-on debugging across AI model providers.

Own technical relationships with strategic enterprise customers throughout Moveworks implementations. Design AI agent solutions, enterprise integrations, workflows, and automations using APIs, iPaaS tools, cloud functions, and scripting. Advise customers on agentic AI roadmaps, coordinate cross-functional delivery, develop reusable capabilities, and mentor engineers. The role combines solution architecture, hands-on implementation, technical consulting, executive communication, and up to 25% travel.

Leads health economics and outcomes research and real-world evidence projects for pharmaceutical, biotech, academic, and other partners. Designs and analyzes pharmacoepidemiologic studies, treatment patterns, healthcare utilization, causal inference, and survival analyses. Builds reusable analytical infrastructure, collaborates with scientists, engineers, and product teams, interprets results for stakeholders, supports publications and regulatory work, and ensures compliance with data governance and applicable regulations.

Leads the design and implementation of AI-powered finance workflows, agents, copilots, and integrations across ERP, billing, procurement, and other financial systems. Establishes AI architecture, governance, security, observability, and responsible-use standards while partnering with Finance, IT, Security, Legal, and Audit. Identifies automation opportunities, measures business impact, builds reusable frameworks, and enables nontechnical teams to adopt AI solutions.

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.

Conducts research evaluating AI-generated digital twins against human responses. Designs experiments, statistical measures, human rating studies, longitudinal data collection, and model evaluations. Analyzes large-scale behavioral data, assesses model calibration and simulation performance, specifies analyses for engineers, and co-authors academic or industry publications.

As an Engagement Management Lead, you'll manage strategic customer relationships, oversee project delivery, and ensure quality outcomes while navigating complex customer dynamics.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Own numerical analysis and model parity for compressed, quantized, and optimized autonomous-vehicle models. Develop Jacobian/Hessian sensitivity tools, connect numerical differences to driving behavior, diagnose training instabilities, and build low-overhead observability for distributed training. Investigate floating-point errors, edge cases, and implementation divergence while translating analytical results into deployment decisions and production-ready tooling.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.

Design, develop, optimize, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, orchestration, governance, and observability. Lead cost-performance optimization across production AI platforms, establish engineering standards, contribute to technical roadmaps, lead design reviews, and mentor senior AI engineers. Collaborate with research, engineering, program management, and product teams to deliver scalable, responsible AI products.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Leads Samsara’s GTM AI Engineering team, building production AI agents, copilots, workflow automations, predictive models, and evaluation systems. Owns the shared AI platform, architecture, governance, observability, model routing, cost controls, and CRM intelligence. Partners with Sales, Customer Success, Support, Product, and Engineering to drive adoption and measurable business impact. This hands-on director role involves coding and prototyping approximately 70% of the time while hiring, developing, and guiding the engineering team.

Own AI safety projects for frontier AI labs, including adversarial datasets, red-teaming programs, safety benchmarks, and evaluation systems. Manage client relationships and end-to-end delivery while leading cross-functional teams of technical and subject-matter experts. Collaborate on AI research and data infrastructure initiatives, and drive company-wide strategic projects in a high-intensity, on-site environment.

Build and own evaluation infrastructure, observability tooling, datasets, annotation workflows, and diagnostic systems for production AI agents. Lead experiments across prompts, models, routing, caching, cost, and latency. Diagnose failures across planning, tool use, and agent execution, while partnering with AI Core engineering. Manage and grow the AI Quality team as a hands-on player-coach, contributing directly to production code and technical direction.

The Principal AI/ML Engineer will lead the development of AI/ML platforms, drive technology architecture, ensure quality and integration, and provide strategic leadership while managing a team of engineers.

Leads OuterBox’s internal AI strategy, AI engineering team, and development of client-facing AI services. Identifies market opportunities, converts internal capabilities into priced consulting offerings, oversees production deployments and partner programs, and drives AI adoption, training, and measurement. Partners with executives, clients, engineering, and technology teams on architecture, security, cloud governance, reliability, pricing, and ROI.

Set the technical direction for Rapid7’s LLM and agentic AI systems. Lead architecture, model strategy, fine-tuning, post-training, inference optimization, evaluation standards, and complex cross-team initiatives. Build critical model and inference code, mentor senior engineers, resolve technical trade-offs, and influence product and platform strategy across cybersecurity products.

Sets Rapid7’s company-wide AI technology vision, architecture strategy, and multi-year roadmap for security products. Leads production LLM and agentic AI initiatives, establishes engineering, evaluation, and responsible-AI standards, and guides model and infrastructure investments. Partners with executives, mentors senior engineering leaders, solves high-impact technical challenges, and represents Rapid7 externally. The role spans LLMs, predictive AI, classical machine learning, distributed systems, and scalable AI production infrastructure.

Designs, builds, and ships production LLM and agentic AI systems for cybersecurity products. Leads architecture, model strategy, fine-tuning, post-training, inference optimization, evaluation, and technical roadmaps. Establishes engineering practices, mentors engineers, leads design reviews, prototypes critical model and training code, and partners cross-functionally to deliver scalable, cost-effective AI solutions.

Lead and grow a lean Applied AI engineering team while personally building agentic systems, prototypes, evaluations, and AI-native tools. Partner across Sales, Marketing, Operations, and Engineering to deliver measurable business outcomes, establish AI quality standards, test emerging models, and hand stable solutions to durable owners. The role requires managing engineers while remaining hands-on, connecting technical work to revenue and efficiency metrics, and creating processes for rapid experimentation and delivery.

Lead applied AI research focused on information retrieval, RAG, agentic workflows, ranking, reasoning, and evaluation. Build prototypes, golden datasets, quality metrics, regression detection, and experimentation frameworks; investigate failure modes and validate improvements through rigorous testing. Collaborate with AI and software engineers to productionize successful approaches and stay current with research in RAG, agents, LLM evaluation, and relevance modeling.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to strategic roadmaps, and mentor senior technical staff.