Top AI Engineer Jobs in San Francisco Bay Area, CA

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.

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.

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 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 foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI performance governance, lead design reviews, define technical roadmaps, and mentor senior AI engineering talent.

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.

Designs, develops, and deploys enterprise-grade autonomous AI services and multi-agent workflows. Integrates MCP and A2A protocols, architectures hybrid control flows, secures tool execution, and builds scalable microservices. Applies SRE practices such as fallbacks, circuit breakers, audit trails, and observability. Requires advanced Python, cloud foundation model platforms, containerization, CI/CD, distributed systems, and production LLM deployment experience.

Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, orchestration, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Establish AI governance standards, lead technical reviews, shape long-term platform strategy, and mentor senior AI engineers. Collaborate with engineering, research, product, and program teams to deliver responsible AI products.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, orchestration, cost-performance governance, technical reviews, roadmap development, and responsible AI standards. Mentor senior technical staff and apply advanced optimization techniques to improve model scalability, latency, throughput, hardware utilization, and inference cost.

Build and operate Affirm’s AI platform, including LLM and MCP gateways, agent runtimes, evaluation services, and governance controls. Design highly available backend systems, manage identity, quotas, routing, caching, reliability, observability, and security for AI workloads. Own projects from design through launch and on-call, establish engineering standards, collaborate across teams, mentor engineers, and help scale AI systems into production.

Build and deploy scalable generative AI platform services, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor senior technical staff.

Designs and deploys AI-powered features and scalable AI/ML services across SaaS products. Responsibilities include translating business problems into AI solutions, integrating large language models, preparing data, evaluating model performance, writing Python, building secure deployment infrastructure, and collaborating with product, data, and cloud operations teams. The role requires production AI/ML experience, scalable data infrastructure expertise, AWS familiarity, and modern DevOps practices.

Lead the design, deployment, and operation of production-scale photogrammetry and 3D reconstruction systems. Own computer vision investigations, optimize pipelines for speed and reliability, integrate emerging 3D methods, establish observability standards, mentor engineers, guide technical execution, and expand AI-assisted development workflows.

Design, build, deploy, and operate production AI-native systems spanning applications, APIs, data, cloud infrastructure, DevSecOps, security, observability, and agentic AI. Lead architecture, client engagements, multidisciplinary engineering teams, and technical delivery from ambiguous requirements through production. Build cloud-native platforms, CI/CD pipelines, secure data and context architectures, AI integrations, agent orchestration, evaluations, and production safeguards. Establish reusable engineering standards, tooling, and practices for enterprise AI adoption.

Build and own AI-powered procurement systems, including distributed services, data pipelines, and cloud infrastructure. Explore and apply cutting-edge AI and LLM technologies while solving complex technical challenges in a fast-paced startup. The role requires end-to-end ownership, adaptability, collaboration, and mentorship, with occasional extended workweeks expected.

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

AI Engineer Interns will research, design, evaluate, and productionize AI-powered data capabilities. Responsibilities include running AI/ML experiments, developing agent self-improvement and continuous-learning systems, collaborating with product engineers, building evaluation and training infrastructure, optimizing performance, designing APIs, and contributing across the full development lifecycle.

Lead development and architecture of intelligent agents that automate complex supply chain workflows. Design distributed planning systems, AI model deployment, safety constraints, reliability frameworks, observability, and evaluation benchmarks. Partner with product and executive leadership on technical roadmaps, mentor engineers, collaborate cross-functionally, engage customers, and support hiring. The role requires strong software engineering, AI/ML, APIs, data modeling, databases, model evaluation, automated testing, telemetry, and data-drift monitoring experience.

Build and deploy production-ready AI applications using pretrained LLMs and agentic systems. Responsibilities include designing orchestration pipelines, optimizing prompts, creating evaluation and benchmarking frameworks, developing data curation pipelines, and ensuring safe clinical application outputs. The role involves collaborating with clinical, product, infrastructure, and engineering teams while mentoring engineers and establishing technical standards.

Designs and maintains scalable AI/ML, computer vision, and MLOps systems for scientific imaging data. Develops deep learning models for segmentation, classification, phenotypic profiling, registration, and feature extraction; builds evaluation frameworks, cloud data pipelines, and high-performance GPU workflows. Collaborates with scientists, analysts, product leaders, and engineers to translate imaging needs into technical solutions across biomedical and clinical datasets.

Build and own Mercor’s AI evaluation products, including APIs, data models, user interfaces, scorecards, failure reports, dataset registries, versioning, onboarding, and usage views. Run APEX benchmarks for frontier models, publish defensible results, analyze model failures, and work directly with enterprise and laboratory customers to shape the roadmap. The role requires strong full-stack development, product ownership, communication, and comfort working in an ambiguous startup environment.