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

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.

Lead high-volume, full-cycle recruiting for operations roles (operators, teleoperators, technicians). Source at scale, coordinate screens/interviews/onboarding, partner with operations leaders to forecast headcount, track and improve hiring metrics, and streamline ATS workflows and automation to maintain fast, high-quality hiring while delivering a positive candidate experience.

Own hardware product strategy for next-generation robots: define requirements for mobility, sensing, and end-effectors; translate field learnings into specs; drive delivery through DVT to production; prioritize reliability, serviceability, and tradeoffs across cost, capability, and timeline while aligning cross-functional teams.

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

The Marketing and Communications Lead will shape Dyna's narrative, manage PR, lead content strategy, and attract talent while enhancing brand visibility in the robotics and AI sectors.

The Research Engineer/Scientist will develop AI models for robotic manipulation, design learning algorithms, analyze results, and collaborate with cross-functional teams to enhance performance.

The Deployment Success Manager will oversee customer deployments, manage operational execution on-site, and ensure stakeholder communication, tracking project timelines and addressing any operational challenges that arise during deployment.

The AI Data Strategist role focuses on defining data requirements to drive model improvement across robotic platforms, emphasizing data collection priorities, evaluation frameworks, and lifecycle observability.

Develop and lead software for robotic systems, focusing on control, sensing, teleoperation, and integration with AI and hardware teams.