Top Tech Jobs & Startup Jobs in San Francisco, CA

Conduct applied AI research and build foundation models from design through training, evaluation, validation, and deployment. Partner with data scientists, engineers, and product managers to deliver scalable AI products. Lead research initiatives involving large deep learning models, optimization, self-supervised learning, robustness, explainability, and fine-tuning. Translate research into production systems, develop platform or solution-level code, and guide long-term research agendas with measurable customer and business impact.

Leads applied AI research and strategic direction for foundation models and large-scale machine learning systems. Responsibilities include designing, training, evaluating, validating, and deploying models; pursuing novel research; collaborating with engineering, product, and data science teams; mentoring scientists and managers; translating research into business outcomes; and representing the company in the research community. Focus areas may include LLMs, behavioral models, optimization, fine-tuning, data preparation, and scalable production AI.

Architect and modernize highly available card-account and transaction platforms, migrating monoliths to microservices and event-driven systems. Define technical vision, architecture, API standards, and engineering best practices while ensuring scalability, security, performance, and operability. Provide hands-on technical leadership, mentor engineers, lead cross-domain initiatives, conduct architectural reviews, and collaborate with engineering, product, and architecture leaders.

Develop and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, infrastructure utilization, latency, throughput, and cost. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to foundational AI roadmaps, and mentor engineering staff.

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 products. Research emerging methods, optimize large deep learning models, develop platform or solution code, publish findings, and pursue an independent research agenda focused on impactful customer applications.

Design, develop, deploy, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Optimize AI systems for scalability, latency, throughput, hardware utilization, and cost. Define technical roadmaps and governance standards, lead design reviews, integrate heterogeneous AI systems, and mentor senior technical staff.

Build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML applications, distributed data pipelines, cloud-based infrastructure, model monitoring, retraining, CI/CD automation, testing, governance, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while optimizing algorithms and supporting large-scale containerized systems.

Designs human-centered digital and physical product experiences for Capital One. Responsibilities include creating journey maps, service blueprints, high-fidelity designs, and prototypes; applying interaction, visual, and service design expertise; using research, data, technology constraints, and design systems; facilitating collaboration with product, technology, and business partners; and communicating design decisions to diverse audiences.

Leads enterprise AI/ML technical programs from strategy through production, including platform modernization, scalability, governance, and delivery. Owns executive communications, roadmap alignment, investment prioritization, architectural decisions, and program risk management. Establishes TPM practices and performance frameworks, mentors TPM leaders, and collaborates with researchers, engineers, business leaders, and cross-functional stakeholders to deliver reliable AI/ML capabilities.

Designs, develops, tests, and maintains complex Workday Finance integrations using web services and related technologies. Leads integrators and configurators, supports Finance modules, automates workflows, performs code reviews, improves business processes, and partners with product owners and stakeholders in an Agile environment.