Top AI Automation Jobs in San Francisco Bay Area, CA

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.

Design, build, and operate production AI automations and agentic workflows for Toast’s customer care systems. Integrate LLMs with Salesforce Service Cloud, Amazon Connect, and workflow platforms; develop autonomous actions with human oversight; test, monitor, and improve system reliability, accuracy, latency, and cost. Collaborate with software engineers, Salesforce developers, and care operations stakeholders to translate workflows into scalable intelligent solutions.

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.

Leads AI engineering strategy, team development, and delivery of scalable AI and machine learning solutions. Provides technical guidance on solution design, MLOps, automation, DevSecOps, testing, and operational readiness. Partners with business, data, security, architecture, and technology stakeholders to translate requirements into secure, reliable capabilities. Oversees delivery risks, dependencies, quality, governance, responsible AI practices, and continuous improvement while communicating progress and outcomes to leadership.

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.

Lead development of agentic AI systems that autonomously plan, execute, analyze, and optimize laboratory experiments. Build integrations with robotic platforms and analytical instruments, implement closed-loop learning, deploy reliable containerized services, and ensure experiment data supports downstream model training. Own the platform roadmap, reliability, adoption, and technical direction while managing and mentoring a team of scientist-engineers. Collaborate with chemists, biologists, automation engineers, NVIDIA, vendors, and senior R&D 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.

Engineer agentic AI and automation integrations connecting planning systems with laboratory instruments, robotic platforms, ELN/LIMS, and data pipelines. Build multi-agent workflows with orchestration, state management, tool integration, error recovery, and human oversight. Deploy containerized services using Docker, Kubernetes, Argo, GitOps, and CI/CD. Collaborate with scientists, automation engineers, and NVIDIA researchers to transition prototypes into reliable autonomous experimentation systems for drug discovery.

Design and analyze rigorous messaging experiments using A/B testing and causal inference. Build measurement frameworks, data pipelines, dashboards, automated analyses, and scalable data solutions for messaging campaigns. Partner with CRM, Product, Algorithms, and Engineering teams to translate ambiguous business questions into actionable recommendations and experimentation standards. Support messaging strategy across channels, timing, content, targeting, and new business verticals.

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

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.

Build and operate AI-powered automation, CI/CD systems, distributed services, developer tools, validation platforms, and code-intelligence capabilities. Own significant systems from architecture through production, improve engineering workflows using measurable results, scale reliable automation and delivery platforms, and integrate capabilities through APIs and MCP. Establish operational practices, participate in on-call support, conduct technical reviews, mentor engineers, and lead cross-team delivery of dependable engineering platforms.

Design, build, and operate AI-powered automation, CI/CD systems, distributed services, developer tooling, and code-intelligence platforms. Own significant capabilities from architecture through production, improve engineering workflows using measurable evaluations, scale reliable automation and delivery systems, and operate secure multitenant services. Lead technical reviews, define reliability practices, integrate platforms through APIs and MCP, mentor engineers, and drive ambiguous technical problems to dependable production outcomes.

Designs, develops, evaluates, and deploys AI and data-driven solutions using machine learning, deep learning, NLP, LLMs, intelligent agents, prompt engineering, and RAG. Builds scalable data pipelines, experiments with models, deploys AI services through APIs and cloud infrastructure, and monitors system reliability and performance. Collaborates with engineering, product, and customer-facing teams to translate business needs into production-ready solutions while addressing risks and continuously improving AI capabilities.

Intern will identify productivity challenges and process gaps across global sales teams, prioritize improvement opportunities, and build AI-enabled solutions using Microsoft Copilot, agents, chatbots, Excel automation, and related tools. The role also supports sales process documentation, implementation planning, stakeholder collaboration, governance, and measuring efficiency improvements and business impact.

Build and deploy AI agent systems that automate complex business processes. Analyze workflows, design and configure agents, integrate AI with enterprise applications, develop connectors and APIs, and establish evaluation and monitoring systems. The role requires expertise in cloud AI services, LLMs, NLP, Python, integration platforms, conversational AI, RAG, vector databases, APIs, and SQL/NoSQL systems. The engineer will also explore emerging AI technologies and mentor junior developers and system administrators.

Lead design and delivery of agentic AI systems and LLMOps on AWS to generate, validate, and deploy infrastructure-as-code (Terraform). Build multi-agent applications, RAG pipelines, and AIOps for cloud operations; integrate AI into CI/CD and ITSM. Set technical direction, establish guardrails/policy-as-code, curate reusable Terraform modules, and mentor engineering teams.

Leads AI presales strategy, customer advisory, technical solution architecture, and team development. Translates business needs into AI use cases, prototypes, business cases, roadmaps, and repeatable offerings. Partners with sales, engineering, consultants, and technology vendors to support complex pursuits, executive briefings, demonstrations, proofs of concept, and AI business growth. Promotes responsible AI governance, tracks pipeline performance, develops thought leadership, and remains hands-on in high-impact customer engagements.

Build AI-powered software automating hiring, finance, capital planning, contract analysis, and operational workflows. Develop production features using LLM APIs, MCP servers, and agentic frameworks; integrate enterprise systems; transform expert processes into structured data and generated work; and collaborate directly with finance, legal, recruiting, accounting, and delivery teams. The role emphasizes autonomy, rapid execution, product judgment, and forward deployment with business experts.

Identifies business opportunities for AI and automation, gathers and documents requirements, develops business cases, supports solution design and delivery, facilitates stakeholder collaboration, assists with testing and change management, and tracks project outcomes, reporting, and benefits realization.

Build applied AI and automation solutions that transform operational workflows into secure, observable, production-ready services. Develop AI agents, orchestration workflows, APIs, integrations, evaluation frameworks, and automation infrastructure across Azure, .NET, C#, Python, React, and SQL. Improve AI quality through grounding, retrieval, evaluation, observability, and responsible AI practices. Partner with engineers, stakeholders, and end users to translate business processes into effective technical solutions.

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.

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.

Opportunistic role for exceptional candidates to define their own mandate applying frontier AI to biomedical science. Work spans agent platform, science, infrastructure, product, and commercial tasks with end-to-end ownership. Must be highly autonomous, AI-native, collaborative, and able to work onsite in South San Francisco.

Lead and scale a Customer Engineering team supporting production AI workloads. Own operational health, customer escalations, and technical issue resolution. Partner with Product and Engineering to translate customer feedback into product improvements. Drive automation, diagnostics, documentation, and tooling to reduce recurring issues and improve customer outcomes.