Top AI Automation Jobs in San Francisco Bay Area, CA

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.

Leads advanced analytics, AI, automation, and scientific computing initiatives supporting pharmaceutical clinical development. Translates scientific and business requirements into scalable analytical products, software specifications, decision-support tools, and production automation. Partners across biometrics, clinical development, IT, and business teams; evaluates emerging AI technologies; improves regulated workflows; establishes value metrics; drives adoption and change management; and mentors technical talent.

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.

Build and deploy AI-enabled solutions for Marketplace operations and commercial challenges. Identify opportunities, frame ambiguous business problems, develop prototypes using enterprise data and AI/LLM tools, validate business value, and transition successful solutions into production. Partner with business, product, technology, and data teams; drive adoption, measure outcomes, coordinate initiatives, mentor team members, and enhance reusable platforms and frameworks.

Own the enterprise AI strategy, roadmap, governance, investment priorities, and measurement framework. Identify and prototype high-value AI use cases, guide pilots into production, establish responsible-AI standards for sensitive data, and communicate outcomes to executives. Build adoption through enablement and reusable practices while coordinating technology, business, legal, security, data, and vendor partners. Lead the emerging AI strategy function and hold teams accountable for delivery and value realization.

Supports enterprise growth through strategic market research, business analytics, account intelligence, and AI innovation. The intern will research companies and competitors, analyze market trends, prepare strategic presentations, and help design and test AI agents and intelligent workflows for automated research and reporting. The role collaborates with Sales and Marketing while identifying ways to improve productivity and decision-making using generative AI and emerging technologies.

Build and maintain AI-powered workflows, agents, automations, internal tools, and production systems for creative ideation, copy generation, video pipelines, reporting, and client delivery. Integrate LLMs and third-party APIs, ship rapidly, iterate based on real usage, and develop practical tools that improve marketing operations.

Lead an engineering team building scalable detection and mitigation systems that prevent harmful misuse of frontier AI models. Set technical roadmaps, design and ship production services, pipelines, tooling, and automation; partner with Research, Product, and Security; ensure reliability, monitoring, performance, and operational excellence; and hire and develop engineers across backend, data systems, and applied ML.

Develop and deploy multimodal machine learning systems for autonomous drug-discovery laboratories. Responsibilities include computer vision, vision-language models, sensor and laboratory data integration, predictive modeling, agentic recovery workflows, evaluation pipelines, and scalable low-latency ML infrastructure. The role collaborates with scientists, robotics engineers, and software engineers to create reliable closed-loop experimentation systems.

Develop and maintain production AI-powered software for mission-critical USPS systems. Design distributed, microservice-based architectures, improve reliability and scalability, identify bottlenecks, and modernize existing systems. Work across the technology stack, communicate architectural decisions, mentor junior developers, and use AI and automation tools to improve engineering outcomes. The role is remote and requires the ability to obtain Public Trust clearance.

Own enterprise customer deployments of agentic AI and hybrid automation solutions from discovery through production. Build AI agents using Claude, Gemini, and GPT; integrate RPA, document AI, Python services, and enterprise systems; deploy workloads on Google Cloud; and implement evaluations, guardrails, monitoring, and CI/CD. Mentor automation developers, lead customer workshops, measure ROI, and provide field insights to Product and Engineering.

Build, test, and support AI-powered applications and workflow automations using Microsoft Power Platform, GPTs, agents, APIs, and automation tools. Configure prompts, integrations, and knowledge sources; troubleshoot workflows; conduct testing and UAT; document solutions; support deployments and users; and follow information security practices. The role offers guided development for an early-career engineer with foundational programming, AI, automation, and software skills.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

Build and deploy AI-powered automation workflows for marketing, growth, content, sales collateral, social distribution, and community operations. Responsibilities include auditing existing processes, automating blog production and publishing, encoding brand voice, integrating platform APIs, implementing human approval steps, documenting workflows, and handing them off to the team.

Design and implement AI-native workflow orchestration and automation systems for legal services. Build integrations between AI models, CRMs, document systems, client portals, and internal tools. Develop agent workflows, vector retrieval and observability pipelines, monitor reliability and performance, troubleshoot integrations, and support governance, compliance, and scalable client experiences.

Design and implement technical AI governance frameworks for generative and agentic AI systems. Assess ethical, safety, security, and sociotechnical risks; evaluate architectures, data flows, permissions, tool integrations, and human oversight; and develop practical mitigations and controls. Partner with engineering, product, legal, security, and business teams to embed responsible AI practices, conduct red teaming and risk assessments, support regulatory alignment, and advance AI literacy across the organization.

Own Cerby’s AI agents for API catalog generation, browser workflow creation, and self-healing automation. Take existing systems into production using durable workflows, observability, evaluation benchmarks, cost controls, and security protections. Responsibilities include detecting and repairing automation drift, managing credential isolation and prompt-injection risks, defining threat models, partnering with engineering teams, writing design documentation, mentoring contributors, and providing on-call ownership after launch.

Leads consulting engagements that design, build, evaluate, and deploy enterprise agentic AI and infrastructure automation solutions. Responsibilities include multi-agent orchestration, LLM and model-platform integration, RAG, tool and API connectivity, guardrails, observability, testing, and production rollout. The role advises client executives, translates business needs into AI strategies, mentors engineers, develops reusable architectures and evaluation assets, and drives responsible, measurable AI outcomes.

The AI and Automation Solutions Developer architects, builds, tests, deploys, and maintains production-ready AI applications, agents, integrations, and automated workflows. The role develops Power Platform solutions, APIs, enterprise integrations, and internal applications; implements security, privacy, governance, monitoring, and documentation; evaluates emerging AI technologies; measures operational impact; and partners with data, clinical, IT, quality, operations, and vendor teams to deliver scalable healthcare solutions.

Designs, builds, and optimizes AI-driven automation workflows that reduce manual work and improve business efficiency. Responsibilities include integrating CRM systems, APIs, and automation platforms; developing chatbots, AI assistants, and prompt systems; monitoring performance; troubleshooting errors; and collaborating with marketing, operations, and technology teams. The role also requires staying current with AI tools and automation trends.

Build and deliver enterprise AI agent solutions using Microsoft Copilot Studio, Microsoft Foundry, Microsoft 365 Copilot, Azure, and Power Platform. Design agent architectures, integrations, grounding, evaluations, automations, and responsible AI controls. Implement governance, security, compliance, lifecycle management, ALM, DLP, identity, monitoring, and cost optimization. Advise customers, translate business requirements into production solutions, create reusable technical assets, and facilitate technical discussions across customer and internal teams.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes trusted metrics, partners with Finance, Product, Commercial, Engineering, and Data Engineering, and mentors scientists. The role drives adoption of governed self-service analytics and communicates assumptions, uncertainty, and business trade-offs to leadership.

Lead AI partnership strategy and execution to grow AI Frontier Labs alliances. Own alliance performance, co-sell pipeline, joint GTM and solution development, partner account mapping, enablement, marketing, and executive alignment to scale generative AI adoption and revenue.

Build and maintain production-grade automation for real-world AI evaluations. The role owns end-to-end workflow orchestration, environment setup, artifact capture, reproducible datasets, scorecards, diagnostics, and release-triggered testing. It also develops agent- and LLM-driven workflows to evaluate customer use cases at scale, converting manual experiments into reliable standing protocols and actionable findings.