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

Analyze complex data and develop managed data solutions, business intelligence tools, dashboards, metrics, and self-service capabilities. Partner with business and technology teams to deliver strategic insights, manage data quality, metadata, lineage, security, and access governance. The role requires scripting, querying, visualization, Snowflake development, and familiarity with cloud services and data governance.

Leads AI engineering teams responsible for foundational AI infrastructure, including model access, LLM inference, optimization, guardrails, evaluation, governance, observability, and cost management. Oversees development and deployment of scalable AI systems, makes build-versus-buy decisions, establishes technical vision, applies advanced research, and develops high-performing engineering talent.

Leads product management for Capital One’s Consumer and Developer Experience organization, driving strategy, innovation, technology-enabled customer experiences, and cross-functional alignment. Oversees platforms supporting digital engagement, developer tools, personalization, AI-driven software development, risk, and supplier capabilities. Applies human-centered, business-focused, technology-driven, integrated problem-solving, and transformational leadership principles to deliver scalable product outcomes.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Partner with data scientists, software engineers, ML engineers, and product managers to deliver scalable AI products. Research areas include deep learning, graph and sequential models, model optimization, self-supervised learning, robustness, explainability, RLHF, LLM fine-tuning, and data preparation. Own research agendas, publish innovative work, and translate technical findings into business outcomes.

Lead development of scalable, responsible AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize AI training and inference for scalability, cost, latency, throughput, and hardware utilization. Partner with engineers, scientists, technical program managers, and product managers to deliver production AI systems and shape the long-term technical roadmap.

Lead a cross-organizational program to transition engineering to an AI-driven, self-optimizing ecosystem with robust telemetry and evaluation. Orchestrate strategy and execution, mitigate technical risk, build and grow a TPM practice, align product/engineering/design/data science, and deliver measurable business impact.

Leads the technical program management practice, develops TPM teams, and oversees delivery of complex, scalable, and resilient technology programs. Identifies and mitigates technical risks, simplifies complex decisions, aligns product and engineering stakeholders, and drives execution across organizational boundaries. The role emphasizes distributed systems, cloud architecture, reliability engineering, observability, incident response, resilience, Agile delivery, and operating effectively in regulated environments.

Design, develop, deploy, and support scalable AI platform components, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize production AI systems for cost, latency, throughput, and scalability. Contribute to foundational AI strategy and roadmap while collaborating with engineering, research, product, and program teams. Lead and mentor engineers and apply emerging AI research to responsible production systems.

Leads the design, development, and productionization of machine learning systems at scale. Builds and validates models, data pipelines, cloud-based ML architectures, and application code; automates testing and deployment; monitors and retrains production models; and ensures high availability, performance, security, governance, and responsible AI practices. Collaborates with Product, Data Science, and Agile engineering teams to deliver optimized ML applications.

Leads data engineering initiatives by designing, developing, testing, implementing, and supporting cloud-based data solutions. Collaborates with Agile teams, product managers, and machine learning engineers; works with distributed systems, databases, streaming applications, and data warehouses. Performs code reviews and unit testing, optimizes performance, explores emerging technologies, and mentors engineering team members.