Deployment Engineer

• Own real deployment problems end to end, from understanding workflows and operating constraints to diagnosing failures in the field.
• Work directly with robotic systems in commercial and industrial environments where conditions are variable and imperfect.
• Translate deployment friction into clear research and product questions, including missing capabilities, data needs, evaluation gaps, and stack improvements.
• Build and adapt systems across the deployment loop, including data collection, task-specific model adaptation or fine-tuning, and evaluation design.
• Work closely with core research and platform teams so field learnings become reusable capabilities rather than one-off fixes.
• Contribute technical expertise across areas such as perception, navigation, manipulation, or teleoperation.
• Help shape how humanoid robots are deployed, improved, and scaled across real-world use cases.

What They Are Looking For

• Deep expertise in at least one of the following areas: perception, navigation, manipulation, or teleoperation.
• Experience deploying AI, robotics, autonomous, or embodied systems in real environments, not only controlled laboratory settings.
• Ability to work beyond a single specialization when deployment challenges require it.
• Experience collecting, cleaning, or curating real-world deployment data for model or system improvement.
• Experience designing evaluations, benchmarks, or testing approaches for systems interacting with real-world environments.
• Strong communication skills, with the ability to explain complex technical ideas to both technical and non-technical stakeholders.
• High ownership and comfort working through ambiguity in fast-moving technical environments.

• Experience moving between software engineering, research, operations, and user or customer conversations.
• Previous work in a forward-deployed, field robotics, customer-facing engineering, or similarly hands-on technical role.
• Experience building trust with users while maintaining a strong technical point of view.
• Curiosity around AI tools and a willingness to use AI as part of everyday engineering and problem-solving workflows.

Benefits

• Competitive compensation and equity participation.
• Equipment stipend to support an effective working setup.
• Travel opportunities connected to the work.
• Dedicated learning and skills development budget.
• Generous leave and parental leave.
• Support for software subscriptions and AI infrastructure or credits.
• Budget for language and presentation skills development.