Top AI Engineer Jobs in San Francisco Bay Area, CA

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.

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.

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.

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.

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.

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.

Build and deploy scalable, responsible AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architectures, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to platform roadmaps, and mentor engineering staff.

Design, build, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor engineering teams.

Leads enterprise AI engineering strategy and multi-team execution. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and production optimization. Makes technology decisions, scales reusable AI infrastructure and responsible AI practices, partners with research and risk teams, and develops engineering talent. Requires strong hands-on expertise in AI systems, cloud deployment, model optimization, and organizational leadership.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Build and deploy generative AI solutions, including APIs, MCP servers, AI agents, LLM integrations, and full-stack applications. Develop scalable AWS cloud services, observability systems, evaluation pipelines, and production agent workflows. Collaborate with Product, UX, Security, Legal, and engineering teams to design interfaces, adapt AI tools to company codebases, and support customer experience and developer productivity use cases.

Build and operate production AI systems for FDA regulatory intelligence, including evaluation pipelines, document ingestion and retrieval, multi-agent workflows, data pipelines, cloud infrastructure, and secure multi-tenant services. Collaborate with former FDA experts to translate domain judgment into product requirements and evaluation cases. Own architecture, reliability, observability, cost, security, and rapid 0-to-1 delivery across the stack.

Build and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Own complex architectures, reliability SLOs, security and compliance reviews, technical roadmaps, and cross-functional delivery while mentoring engineering teams.

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.

Leads the design and implementation of AI-powered finance workflows, agents, copilots, and integrations across ERP, billing, procurement, and other financial systems. Establishes AI architecture, governance, security, observability, and responsible-use standards while partnering with Finance, IT, Security, Legal, and Audit. Identifies automation opportunities, measures business impact, builds reusable frameworks, and enables nontechnical teams to adopt AI solutions.

Own numerical analysis and model parity for compressed, quantized, and optimized autonomous-vehicle models. Develop Jacobian/Hessian sensitivity tools, connect numerical differences to driving behavior, diagnose training instabilities, and build low-overhead observability for distributed training. Investigate floating-point errors, edge cases, and implementation divergence while translating analytical results into deployment decisions and production-ready tooling.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.