Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Leads machine learning engineering teams while designing, deploying, scaling, monitoring, and governing production ML models and platforms. Responsibilities include building distributed ML systems, cloud architectures, data pipelines, CI/CD automation, model retraining, responsible AI practices, and resilient production services. The role combines hands-on technical leadership with mentoring, recruiting, talent development, and cross-functional collaboration with Product and Data Science teams.

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.

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.

Leads Capital One’s Developer Experience Telemetry workstream, defining strategy and guardrails for collecting software development data. Builds unified data products, developer tools, usage dashboards, and cost-optimization mechanisms while balancing privacy, security, compliance, and risk. Establishes rule-based and model-based evaluation frameworks, measures developer productivity and ROI, and drives data-informed decisioning. Partners with Engineering, Machine Learning, Cyber, and Enterprise Data teams while influencing senior executives and leading cross-functional product initiatives.

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.

Leads product management for infrastructure, cloud operations resilience, enterprise connectivity, and network platforms. Drives product strategy, customer-focused solutions, technology innovation, business outcomes, risk mitigation, and cross-functional organizational alignment. Requires translating business strategy into digital products and guiding modernization across data centers, infrastructure, networking, and zero trust architecture.

Owns a portion of the AirKey product roadmap, partnering with design, business, technology, external clients, and sales to commercialize authentication technology. Drives frontend and backend capabilities, customer and client experiences, product strategy, risk mitigation, cross-functional alignment, and innovative digital solutions. Requires strong communication, judgment, client focus, multitasking, and independent decision-making.

Lead the strategy, vision, and product team for enterprise AI/ML platforms supporting model training, model serving, and generative or agentic AI applications. Partner with platform leaders to build integrated capabilities, improve user experiences for data scientists and developers, drive adoption, and deliver business value. The role requires technical product leadership, AI/ML expertise, cross-functional influence, and people management.

Leads applied AI research and teams developing foundation models and production-scale machine learning systems. Sets research strategy, mentors applied scientists, partners with engineering and product leaders, and translates advanced AI research into customer-facing products. Requires expertise in large-scale deep learning, LLMs, optimization, model deployment, research publications, and organizational leadership.