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

Leads and grows multiple software engineering teams while mentoring engineers and partnering with product managers. Oversees full-stack solution design, development, testing, deployment, resilience, scalability, and support. Drives engineering practice improvements, innovation, Agile collaboration, AWS deployments, and modernization of monolithic applications into microservices.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI-powered products. Research state-of-the-art methods in language and vision-language modeling, optimize training and inference, develop platform or solution code, and translate research findings into customer-focused business outcomes.

Lead development of responsible, scalable AI systems, including foundation-model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize production AI systems for scalability, latency, throughput, hardware utilization, and cost. Partner with engineering, research, program management, and product teams while shaping the technical vision and roadmap for foundational AI platforms.

Lead DevOps engineering initiatives and developer teams building cloud-based solutions for regulatory and customer needs. Design and deliver systems using cloud-native technologies, microservices, machine learning platforms, containers, infrastructure-as-code, deployment tools, and distributed databases. Mentor engineers, evaluate emerging technologies, collaborate with product managers, and promote iterative Agile delivery practices.

Leads business analytics strategy, data infrastructure, product analytics, measurement frameworks, experimentation, and data-driven strategy for AI-powered customer assistance channels. Connects customer experience and operational efficiency outcomes while partnering with Product, Technology, Operations, and Finance. Manages and develops analysts, oversees concurrent projects, builds business requirements, and drives executive-level decisions supporting growth, profitability, and service improvements.

Lead technical vision and software engineering excellence for Capital One’s Card Tech team. Build production-ready, AI-native systems; design distributed, scalable, fault-tolerant architectures and API platforms; ensure performance, security, and operability; decompose complex problems; mentor engineers; recruit talent; and share technical knowledge across the organization.

Lead and scale enterprise product and experience teams to deliver human-centered, technology-driven digital products. Translate strategy into customer-facing solutions, drive business outcomes, solve complex problems cross-functionally, and provide transformational leadership to align stakeholders and mitigate product risks.

Design, develop, test, deploy, and support production AI systems, including foundation-model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI training and inference for scalability, latency, throughput, hardware utilization, and cost. Shape the technical vision and roadmap for foundational AI platforms while partnering with engineers, researchers, program managers, and product leaders. Mentor engineering teams and influence senior cross-functional stakeholders.

Designs, develops, tests, deploys, and supports foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develops optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost. Partners with engineering, research, product, and program teams; contributes to technical vision and roadmaps; and mentors teams while applying emerging AI research to production systems.

Manage OneTag platform onboarding, tag stewardship, data quality and observability, and process optimization for Consumer Behavior Analytics. Partner with product and technology teams to improve onboarding, establish preventative and detective data controls, and replace manual workflows with scalable automation. Lead cross-functional alignment, communicate progress and risks, resolve ambiguity, and advance enterprise frameworks, compliance workstreams, and stewardship initiatives.