Top Tech Jobs & Startup Jobs in San Francisco, CA

Leads product strategy and operations for AI/ML platforms, including roadmap prioritization, operational frameworks, adoption of generative and agentic AI, and internal user experience improvements. Partners with Product, Engineering, TPM, and business teams to translate vision into execution, manage programs, develop teams, and drive strategic decisions through quantitative and qualitative analysis.

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

Leads project management and operations for a large design organization. Develops operational strategy, manages cross-functional design and program teams, oversees standards and practice management, and drives portfolio prioritization, resourcing, metrics, and complex initiatives. Partners with executive, product, technology, and design leaders to scale impact, improve processes, and operationalize human-centered programs. Requires substantial project and people management experience, with preferred expertise in design operations, design systems, SDLC/PDLC, and design tools.

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, reliability, and hardware utilization. Own complex architectures, define reliability objectives, ensure security and compliance, influence technical roadmaps, and mentor engineering teams.

Conduct applied AI research and lead development of foundation models from design through production deployment. Collaborate with data scientists, software and machine learning engineers, and product managers to deliver scalable AI products. Research advanced methods, optimize training and inference, evaluate model performance, and translate research into production impact. The role also involves owning research agendas, publishing or advancing machine learning methods, leading cross-functional initiatives, and mentoring junior researchers or engineers.

Leads the design, development, deployment, and optimization of enterprise AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Establishes responsible AI standards, shapes long-term platform strategy, makes technology decisions, scales engineering practices, partners with research and risk teams, and leads multi-team engineering organizations. Mentors and develops talent while delivering scalable, secure, and compliant AI products.

Leads the enterprise vision and strategy for Capital One’s developer experience and context ecosystem. Owns measurement frameworks and a portfolio of data products that assess software development effectiveness. Translates developer experience metrics into strategic organizational improvements that help engineering teams build software faster, with higher quality and less friction. Leads product strategy, cross-functional alignment, innovation, and enterprise platform initiatives.

Designs, develops, deploys, and supports production AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimizes large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Owns complex architecture, defines reliability objectives, leads technical reviews, partners cross-functionally, and mentors engineering colleagues.

Build, deploy, optimize, and monitor production machine learning models and infrastructure. Responsibilities include developing ML applications, constructing data pipelines, operating distributed systems and cloud platforms, automating testing and deployment, retraining models, and applying responsible AI practices. Collaborate with Product, Data Science, and Agile teams to deliver scalable solutions using modern ML frameworks, containerized systems, and cloud architectures.

Leads project and program management across feasibility, planning, prioritization, implementation, and closeout. Establishes structure and standards in complex environments, drives cross-functional delivery, manages stakeholders, develops high-performing teams, and uses metrics to improve operational efficiency and team effectiveness. Requires extensive project or program management and people leadership experience, with preferred expertise in operations, learning and development, MBA, and relevant certifications.