Top AI Engineer Jobs in San Francisco Bay Area, CA

Design and implement multi-agent AI systems for marketing compliance workflows. Build production ML pipelines involving model fine-tuning, prompt engineering, retrieval-augmented generation, evaluation, monitoring, explainability, and feedback-driven personalization. Translate compliance rules into agent logic, ensure auditability across customer deployments, and collaborate with engineering, product, implementation, and compliance teams to deliver reliable AI features.

Design, develop, deploy, and support AI solutions including generative AI applications, agents, workflow automation, document intelligence, and retrieval-augmented generation. Partner with business and IT teams to identify use cases, integrate enterprise platforms and data sources, establish prompt and evaluation standards, support AI governance and security, monitor solution performance, and train employees on responsible AI adoption.

Design, develop, test, deploy, and support production AI systems, including foundation-model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI training and inference for scalability, latency, throughput, hardware utilization, and cost. Shape the technical vision and roadmap for foundational AI platforms while partnering with engineers, researchers, program managers, and product leaders. Mentor engineering teams and influence senior cross-functional stakeholders.

Designs, develops, tests, deploys, and supports foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develops optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost. Partners with engineering, research, product, and program teams; contributes to technical vision and roadmaps; and mentors teams while applying emerging AI research to production systems.

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.

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.

Own the architecture and technical strategy for Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations while establishing AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering leaders to translate business needs into scalable solutions, make build-versus-buy decisions, mentor engineers, and improve business impact through data and user feedback.

Design, build, tune, and launch AI agents and full-stack automations for U.S. federal customers. Architect integrations with enterprise systems while meeting federal security, compliance, procurement, and ATO requirements. Advise customer stakeholders, coordinate cross-functional delivery, support product rollouts, synthesize customer feedback, create reusable solution templates, and upskill agency teams. The role requires hands-on API and LLM systems expertise, customer-facing communication, technical leadership, and up to 25% travel.

Architects and develops AI-powered systems, tools, agents, backend services, APIs, and experimentation frameworks using foundational AI models. Integrates AI capabilities with microservices, AWS, Snowflake, and complex data systems while ensuring scalability, reliability, and maintainability. Translates business problems into technical solutions, prototypes AI applications, collaborates with product and engineering teams, evaluates emerging AI technologies, and documents system designs and integration patterns.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, evaluation and observability capabilities, and workflows that turn insights into action. Work hands-on with large and small models, durable orchestration, cloud deployment, inference-cost optimization, and high-volume observability data. Establish reusable engineering patterns, collaborate with product and platform teams, and mentor through strong technical contributions.

Design, build, and operate agentic AI systems end-to-end for Professional Services. Translate stakeholder needs into secure, scalable multi-agent architectures, define performance benchmarks, manage deployments and incident response, advise leadership, mentor teammates, and maintain runbooks and production tooling including on-call responsibilities.

Build and productionize agentic workflows, RAG pipelines, AI services, backend systems, and ML pipelines for sales automation. Integrate Salesforce, quoting engines, data lakes, and internal platforms using scalable, event-driven architectures. Lead 0-to-1 development of tools for account research, lead prioritization, and quoting while ensuring reliability, observability, performance, and resilience across the GTM technology stack.

Build and deploy AI agents for logistics and transportation across email, phone, and SMS. Improve model reasoning, fine-tune foundational models, integrate AI tools, and create evaluation frameworks for agent performance. Collaborate with engineering, product, and logistics teams to solve complex operational problems. The role requires Python, cloud AI deployment, model optimization, and familiarity with TensorFlow or PyTorch. A bachelor’s degree in a related field is required.

Build and scale machine learning systems for frontier AI data generation and evaluation. Responsibilities include efficient agentic evaluations, model routing, fine-tuning and serving smaller open-weight models, predictive task-difficulty modeling, LLM-as-judge measurement, and productionizing research prototypes. The role requires rigorous experimentation, statistical analysis, strong Python and software engineering, and end-to-end ownership of production ML or software systems.

Work across AI, backend, and frontend: curate and clean writing datasets, fine-tune large language models, run training experiments, develop metrics and eval sets, build backend services in Python/Django, and implement frontend features with React.

Build and deploy LLM-powered features for clinical, patient, and operational workflows. Responsibilities include prompt design, retrieval, tool use, agent orchestration, evaluation harnesses, feedback loops, production operations, and AI engineering tooling. Partner with clinical, product, and safety stakeholders to ensure responsible healthcare-grade AI. Mentor teammates, communicate model behavior clearly, and stay current with emerging AI research and development practices.

Build and own Clera’s AI/ML intelligence layer, including recommendation systems, ranking algorithms, and LLM-powered candidate-role matching. Develop production-ready features, data pipelines, and services while collaborating cross-functionally and shipping frequently. The role requires hands-on experience with the listed development tools, TypeScript, React, startup environments, and a willingness to work six days per week in a San Francisco hacker house.

Build and deploy AI-powered tools, developer workflows, testing pipelines, integrations, and internal interfaces across the organization. Evaluate frontier models and emerging AI tools, improve CI/CD and automated testing, support go-to-market and operations teams, and establish practices for agentic engineering, security, responsible AI, reliability, and cost management. The role requires cross-functional collaboration and rapid prototyping.

Leads AI engineering strategy across clinical, patient, and operational products. Designs and operates production retrieval, orchestration, agentic, and evaluation systems, establishing standards for AI safety, observability, responsible deployment, and reliability. Partners with clinical, product, and executive leaders on the AI roadmap, mentors engineers, evaluates emerging models and tools, and codes regularly in Python.

Build and deploy agentic AI systems, MCP server infrastructure, retrieval pipelines, and evaluation frameworks for an Oracle enterprise knowledge platform. Support AI-assisted sales pursuits through generated content, live demo agents, and presentation assets. Serve as an Anthropic and AI coding agent subject matter expert while partnering with architects, sales, delivery, and client teams to embed AI across the Oracle-Accenture alliance.

Build production-grade agentic AI systems for Cisco Security’s SOC, including agent execution, orchestration, governed tool use, memory, evaluation, telemetry, and trace analysis. Design secure capabilities for cloud, on-premises, sovereign cloud, and air-gapped environments. Own features end to end, improve AI quality and reliability through experimentation and production evidence, collaborate across Cisco and Splunk, contribute to architecture, and mentor engineers.

Build and operate AI-native, full-stack products for sales, marketing, and revenue operations teams. Develop agent workflows, automations, integrations, and production applications for research, enrichment, account scoring, and other go-to-market needs. Collaborate with nontechnical users to understand workflows, test solutions, and improve products. Integrate systems such as Salesforce, Marketo, and Gong while making pragmatic build-versus-buy decisions.

Build and ship AI-native workflows and prototypes for finance/accounting use cases, process large unstructured datasets, iterate with customer feedback, and design analytics-driven evaluation systems to improve product stability and value.

Develop and deploy responsible AI solutions that identify, measure, and mitigate bias across Pinterest’s large-scale machine learning applications, including generative AI, search, and recommendation systems. Collaborate with trust and safety, user modeling, content understanding, and engineering teams to adopt fairness tooling and practices. Mentor engineers, contribute to technical strategy, and bridge advanced research with product impact.

Design, prototype, deploy, and scale production AI workflows, agents, copilots, and automations for enterprise clients. Integrate foundation models with platforms such as SAP, ServiceNow, Salesforce, and internal systems. Translate ambiguous business needs into technical solutions, lead architecture and API orchestration, bridge product and engineering stakeholders, and ensure secure, reliable, governed AI deployments. The role is client-facing and may require 25% to 75% travel.