Top AI Automation Jobs in San Francisco Bay Area, CA

Design and execute test strategies and automation for cloud-native and GenAI/AI-ML systems. Perform functional, integration, performance, security, and API testing; validate model accuracy, fairness, robustness, and data quality; integrate automation into CI/CD; track defects; mentor junior QA engineers.

The AI Automation Engineer will automate internal processes, mentor non-technical staff on building tools, and ensure solutions deliver measurable business impact using AI technologies.

Build scalable QC systems and validation pipelines for RL training data: define quality standards, design metrics and experiments, audit supplier datasets, partner with vendors to diagnose failures, and integrate learnings into infrastructure and vendor tooling.

Build and own an internal AI-powered recruiting pipeline: end-to-end LLM-based sourcing, screening, AI interviews, evaluation logic, and deep integrations with ATS and scheduling to automate hiring workflows.

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.

Lead design, prototyping, deployment, and continuous improvement of AI-powered applications, agents, copilots, and automations. Integrate AI with portals, workflows, data platforms, and AWS; define reusable patterns (RAG, prompts, agent workflows); ensure secure, observable, and cost-effective production systems; measure adoption and ROI; and mentor engineers, contractors, and vendors.

Lead QA Automation for agentic AI products by designing eval pipelines, golden datasets, and automated tests for tool-use, hallucination detection, latency/token monitoring, and regression across models and prompts. Integrate performance testing into CI/CD and collaborate with AI engineers to convert requirements into measurable automated evaluations.

Design and build automation frameworks and evaluation pipelines for agentic AI products. Develop non-deterministic tests, golden datasets, tool-use validation, prompt regression tests, latency/token monitoring, hallucination detection, and collaborate with AI engineers to convert requirements into measurable automated test cases.

The AI Engineer will develop scalable AI systems, automate workflows, and collaborate with business teams to enhance operational efficiency across OpenSesame.

Lead execution of Product Engineering AI and automation initiatives across photonics products. Build and deploy reusable AI/automation tools, transform engineering data into AI-ready systems, and drive cross-functional adoption to improve development, qualification, manufacturing, and reliability processes.

The AI Automation Engineer will apply AI in finance operations to automate processes like AP, AR, and reconciliation, ensuring compliance and performance measurement.

The Director of AI Automation leads AI-driven automation systems, translating strategic goals into practical solutions, managing teams, and enhancing operational efficiency.

Join a talent pool to teach, author, or advise on AI-powered automation. Deliver live workshops, create course materials, design exercises, shape curriculum, review technical content, and support program updates. Part-time (10–15 hrs/week), remote with collaboration in European time zones. Roles span junior to senior levels and emphasize hands-on experience with automation platforms and real-world operational workflows.

The AI Automation Engineer develops AI-driven systems to automate sales and marketing workflows, partnering with various teams to enhance revenue generation and process efficiency.

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.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.