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Dyna Robotics

Exceptional Software Engineer

Redwood City, CA$180k–$400kfulltimemidAdded today

About this role

Dyna Robotics seeks an exceptional software engineer to tackle critical infrastructure challenges in deploying embodied AI models to production robots. You'll own high-impact problems across data pipelines, services, APIs, and internal platforms, with your work reaching real robots in days or weeks.

What you'll do

  • Solve the hardest software problems blocking model deployment from training to production
  • Design and build data infrastructure, pipelines, and services supporting robot operations
  • Develop APIs and deployment systems for AI models in real-world environments
  • Create and maintain internal platform tools for researchers and operators
  • Move fluidly between problems based on what's currently blocking progress
  • Iterate rapidly with feedback from deployed robots in commercial settings

What they're looking for

  • Software engineering fundamentals (architecture, testing, debugging)
  • Data infrastructure and pipeline design
  • Distributed systems or large-scale backend development
  • API design and service architecture
  • Deployment and DevOps practices
  • Python, Go, C++, or similar systems languages
  • Ability to work with ambiguity and incomplete requirements
  • Clear technical communication and reasoning
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Dyna Robotics

Dyna Robotics builds AI-driven robotics platforms that combine embodied AI foundation models with spatial intelligence to enable robots to navigate and operate autonomously across customer sites. The company is hiring software engineers, infrastructure specialists, QA testers, and field deployment engineers to scale its robot deployment systems, training infrastructure, state estimation capabilities, and customer operations.

View all jobs at Dyna Robotics

Likely interview questions

  • Tell us about a time you owned a complex infrastructure problem end-to-end and what you learned.
  • Describe your experience with data pipelines or infrastructure at scale—what went wrong and how did you fix it?