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

Owns employee relations, investigations, performance management, corrective actions, and terminations for a 200+ person, multi-site, shift-based workforce. Partners with managers and site leads, builds HR processes including performance reviews and career paths, analyzes attrition and engagement, and advises on organizational design and headcount planning. Requires strong California employment-law knowledge, sound judgment, discretion, and hands-on experience supporting hourly workers.

Own the reliability of a deployed humanoid robot fleet by diagnosing hardware, firmware, controls, and telemetry failures. Perform hands-on actuator and electronics repair, design test fixtures and mechanical modifications, build fleet health metrics and calibration tooling, analyze robot logs, improve observability, validate firmware and software changes, and document procedures. The role requires strong Python, motor-control and actuator knowledge, hardware troubleshooting, time-series analysis, and cross-functional collaboration.

Lead hardware builds, diagnostics, validation, and quality control for robotic systems. Assemble and troubleshoot electro-mechanical assemblies, develop test plans and fixtures, drive root cause analysis, maintain SOPs and metrics, support deployments, and mentor junior technicians.

The role involves designing and maintaining large-scale ML infrastructure, optimizing distributed training systems, and enhancing computing performance for model training.

Build and maintain the software that moves embodied AI models from training to production, including data infrastructure, services, pipelines, APIs, deployment systems, and internal platforms. Work across team boundaries on the highest-priority technical problems and deliver software to production robots within days or weeks. The role requires exceptional software engineering, strong judgment, clear communication, resilience, and comfort with ambiguity. Robotics experience is welcome but not required.

Own continuous learning for deployed robot fleets: build pipelines to convert deployment data into fine-tuning and policy updates, mine multimodal fleet data for failures and drift, apply RL and reward modeling, create automated real-time monitoring and evaluation, and partner across teams to ship measurable improvements to production robots.

The role involves applied research and engineering for deploying AI in robotics, focusing on infrastructure design, fleet management, and software stability.

Own the data engine and deployment platform that turns field robot behavior into training signal. Define capture, labeling schema, quality standards, annotation pipelines, observability, and tooling for researchers, engineers, operators, and customers. Improve operator and customer experiences, instrument fleets, audit labels, and spend time on-site to observe failures and drive product decisions.

Lead QA function across Dyna's robotics stack, managing a team, developing automated test frameworks, and ensuring hardware behavior validation.

Own day-to-day annotation operations: reporting, Encord pipeline management, throughput and quality metrics, automation of runbooks, people management (hiring, payroll, performance), and vendor/partner coordination to keep labeling projects flowing predictably.