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

Design and build interfaces across HUD’s AI data platform and marketplace. Translate complex agent runs, evaluation results, reward signals, and reinforcement learning workflows into clear, actionable products. Partner with users, researchers, and engineers; conduct user research; ship and refine experiences; establish reusable interaction patterns; and maintain high standards for usability, visual quality, and implementation.

Build reliable software and infrastructure for frontier AI agent research, including environment execution, trajectory collection, evaluations, experiment orchestration, data pipelines, and observability. Partner with research engineers to turn prototypes into reusable, tested systems; improve reproducibility, scalability, cost efficiency, and recovery from failed runs; and own infrastructure from design through operation.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Develop datasets, evaluation methods, validation tools, and quality standards for robotics and embodied AI systems. Translate research needs into dataset specifications, design collection protocols, audit multimodal robotics data, and run experiments analyzing how data quality and structure affect model performance. Collaborate with research, engineering, data providers, and customers to improve robotics data offerings.

Own customer-facing operations across data suppliers and AI labs, including activation, engagement, onboarding, contract and platform issue resolution. Build trust through customer conversations, coordinate internal blockers, and convert recurring feedback into improved processes, platform capabilities, communications, and lightweight workflow systems. The role requires strong judgment, organization, follow-through, CRM familiarity, and comfort working independently in a fast-paced startup environment.

Build and improve HUD’s platform and marketplace for RL training data and evaluations. The Product Engineer will identify user friction, define priorities, design prototypes, and ship full-stack product experiences. Responsibilities include improving agent-run and evaluation workflows, building frontend and backend functionality for onboarding and environment submission, and using user feedback and product signals to iterate. The role requires close collaboration with product, design, engineering, research, and go-to-market teams.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

Design and build interfaces for HUD’s AI agent training, evaluation, and marketplace products. Translate complex reinforcement learning workflows, agent runs, evaluation results, reward signals, and quality feedback into clear, actionable experiences. Collaborate with users, researchers, and engineers to ship polished products, conduct user discovery, improve usability, and develop reusable interaction patterns.

Own the HUD data marketplace product lifecycle, improving vendor onboarding, submission workflows, quality feedback, and marketplace management. Enable research teams to discover, evaluate, and access AI training data and RL environments. Partner with engineering, research, GTM, and operations to simplify complex workflows, define marketplace health metrics, and prioritize product improvements using user feedback and quantitative data.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.