Top AI Engineer Jobs in San Francisco Bay Area, CA

Build and demonstrate practical AI, generative AI, agentic AI, and automation solutions; create reusable technical content, playbooks, prototypes, and learning resources; deliver workshops and hands-on training; coach teams through AI adoption; evaluate emerging technologies; and embed security, governance, testing, observability, and human oversight into enterprise AI implementations.

Lead and mentor an AI engineering team to design, architect, and deploy production-ready multi-agent, LLM-powered systems. Define technical strategy, set architecture and security standards, implement hybrid control flows/state machines, integrate agent-to-tool and agent-to-agent protocols, and collaborate with product and business teams to translate requirements into scalable microservices.

Design, build, evaluate, and operate production AI services and agents. Own data and context design, retrieval, tool integrations, application logic, evaluations, monitoring, and reliability. Improve AI quality through failure analysis, controlled experiments, and production feedback while partnering with architecture, operations, and platform teams to deliver secure, scalable, cost-aware services.

Lead an AI engineering team developing, deploying, and operating scalable foundation-model systems, including LLM inference, similarity search, guardrails, evaluation, governance, and observability. Set technical strategy, optimize model performance and infrastructure costs, make build-versus-buy decisions, operationalize responsible AI standards, translate enterprise goals into roadmaps, and mentor engineering talent while partnering with research, product, data, and platform teams.

Leads software engineering and AI platform initiatives, designing secure, scalable cloud infrastructure for AI/ML workloads. Builds Kubernetes-based platforms, CI/CD pipelines, automation, and infrastructure-as-code for ML deployment and operations. Partners with AI teams on infrastructure requirements, optimizes cloud performance and costs, and promotes responsible AI-assisted engineering practices, governance, validation, and secure SDLC automation.

Own the centralized MCP gateway and AI tool-access platform, including connector onboarding, identity and access controls, provisioning workflows, security guardrails, operations, monitoring, governance, adoption metrics, and technical roadmap. Partner with security, IAM, engineering, and business leaders while mentoring engineers and analysts. Automate access governance and support regulated financial-services requirements.

Design, develop, deploy, and optimize foundational AI systems, including LLM inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, and observability. Lead multi-model orchestration, cost-performance governance, architecture reviews, technical roadmaps, and responsible AI standards. Mentor senior technical staff and collaborate with engineers, scientists, product managers, and program managers to deliver scalable AI products.

Design, develop, deploy, and support foundational AI systems, including large language model inference, agents, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, scalability, and hardware utilization. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor senior technical staff.

Build and operate SoFi’s internal AI platform through production-grade microservices, telemetry pipelines, AI integrations, and developer tooling. Own feature delivery, architecture, testing, deployment, incident response, and operational excellence. Collaborate with partner teams and internal customers, improve platform adoption, participate in design reviews, and mentor engineers. Apply AI-assisted development responsibly while protecting sensitive data and maintaining rigorous security, testing, and governance standards.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization; define reliability objectives; own complex system architecture; lead technical reviews; ensure security and compliance; and mentor engineering associates.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for cost, latency, throughput, and hardware utilization. Architect multi-model orchestration pipelines, establish AI engineering standards, lead technical reviews, and mentor senior AI engineers.

Build agentic AI products that enable customers to create custom detections and workflows from operational camera data. Own projects from customer discovery through design, implementation, production operation, and ongoing optimization. Develop backend services, APIs, and customer-facing experiences; partner with platform, machine learning, product, and design teams. Improve AI accuracy, reliability, performance, and cost while mentoring engineers and shaping technical architecture.

Lead Samsara’s AI initiatives across the software development lifecycle, including developer agents and SRE AI. Set strategy, select experiments, make build-versus-buy decisions, control infrastructure and token costs, scale automated incident response, and build trust in AI-driven actions. Manage and grow a team of five to ten senior engineers while partnering directly with R&D leaders and developers.

Leads the technical direction, architecture, training, fine-tuning, evaluation, and scalable inference of LLM and agentic AI systems for cybersecurity products. The role requires hands-on work with PyTorch, distributed training, model optimization, context engineering, and production AI infrastructure. It also involves setting engineering standards, leading design reviews, mentoring senior engineers, influencing product investments, and delivering organization-wide technical outcomes.

Build and ship production backend services and AI-powered voice agent features. Own customer problems end to end, develop reusable agent modules, improve menu and catalog accuracy, contribute to model fine-tuning, run experiments, and integrate solutions into production. The role involves close customer collaboration, rapid experimentation, and working across Kotlin, Python, and TypeScript in an in-person San Francisco environment.

Architect and build Toast’s Vision AI platform, including video ingestion, processing, storage, inference pipelines, evaluation, and monitoring. Develop production ML services and customer-facing computer vision features, make architecture decisions, write significant code, and guide technical direction. Partner across teams, mentor engineers, establish reliability, security, and privacy standards, and promote AI-assisted development. The role requires deep distributed-systems and cloud expertise, production ML experience, video familiarity, and the ability to take ambiguous ideas from prototype to customer-ready products.

Build and own production Vision AI systems end to end, including model selection, inference pipelines, evaluation, monitoring, and customer-facing features. Shape architecture, collaborate with product, design, and engineering teams, and use AI coding tools to accelerate development. Establish high standards for quality, testing, reliability, security, and privacy while mentoring engineers and taking ambiguous ideas from prototype to production.

Leads an AI engineering team developing scalable generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and agentic workflows. Sets technical vision, makes build-versus-buy decisions, optimizes production AI systems, operationalizes Responsible AI standards, develops team roadmaps, collaborates cross-functionally, and mentors engineering talent.

Apply established machine learning techniques—including clustering, classification, and anomaly detection—to ICS/OT cybersecurity data. Integrate model outputs into batch and near-real-time pipelines, evaluate third-party models, maintain reliable data contracts and observability, and troubleshoot production issues such as data drift and output quality. Collaborate with AI engineers, data engineers, and product teams while clearly communicating model behavior, uncertainty, and downstream use.

Designs, develops, integrates, tests, and deploys AI/ML applications and MLOps solutions for clients. Builds model-serving workflows, APIs, RAG solutions, enterprise integrations, and containerized NVIDIA AI services. Troubleshoots software, model, data, API, and platform issues; creates technical documentation and client handoff materials; contributes to project estimates and implementation plans. This client-facing role also manages scope, schedule, budget, deliverables, and occasional travel to client sites.

Build Superhuman Mail’s AI platform, including MCP tools and standards, agentic workflows, personalization, LLM evaluation systems, and model-selection strategies. The role involves shipping production LLM features, partnering across teams on reusable AI capabilities, and potentially shaping data collection and modeling approaches. Engineers will work primarily with backend systems and applied AI while adapting quickly to evolving technologies and product priorities.

Build full-stack Vision AI products for restaurant operators, including backend APIs, video pipelines, AI model integrations, monitoring, and customer-facing interfaces. Own platform components, contribute to architecture, collaborate across product and engineering teams, and use AI-assisted development tools. The role requires production software experience, cloud infrastructure expertise, event-driven systems knowledge, and comfort working across backend, frontend, data pipelines, and machine learning integrations.

Lead the design, development, and operation of SoFi’s AI developer experience platform, including AI Gateway, model routing, evaluation systems, harness vending, and cost telemetry. Establish engineering standards, guide architecture and cross-team execution, improve CI/CD and operational reliability, mentor engineers, and partner with internal developers to drive adoption of AI coding tools. Ensure secure, compliant use of AI systems and maintain the platform’s technical vision.

Leads architecture and technical strategy for enterprise AI developer experience, including AI gateways, model routing, evaluation platforms, harness vending, and AI economics. Drives cross-team initiatives, resolves distributed-system bottlenecks, establishes engineering standards, leads incident response, evaluates AI tooling, and mentors senior and staff engineers. Requires hands-on experience with LLM architectures, model APIs, AI-assisted development tools, evaluation pipelines, dynamic routing, platform infrastructure, and infrastructure economics.

Conduct research on AI evaluation, rubrics, benchmarks, human judgment, preference data, and agent performance. Design rigorous experiments, validate measurement frameworks, build evaluation pipelines, and create long-horizon real-user benchmarks. Collaborate with engineers, delivery teams, customers, academics, and external labs, while publishing methods and benchmark findings. The role combines applied statistics, messy human data, research prototyping, technical writing, and deployment of research methods in live customer programs.