Staff Autonomy & Planning ML Engineer — Robotics

Knightscope; Sunnyvale (CA); On-site; USD 240,000 - 275,000; Full time

Benefits offered by this job

Medical, dental, vision coverage

Stock options

401(k) with company match

Paid time off

Job summary

Knightscope is seeking a Staff Autonomy Engineer for its Sunnyvale headquarters to own the intelligence-to-action loop across deployed autonomous systems. You will drive perception, planning, and control, delivering end-to-end autonomy capabilities in safety-critical environments.

The role requires deep expertise in planning, learning-based methods, and real-time decision making, with a track record of shipping production-grade robotics software and mentoring engineers.

Qualifications

• 7+ years shipping autonomy, planning, or decision-making systems to production in robotics or safety-critical platforms.
• Deep expertise in behavior planning, imitation learning for real-world robot control, or large-scale ML for real-time decisions.
• Hands-on experience integrating learned planning with classical control and safety constraints.

Responsibilities

• Advance AI and machine learning for embodied autonomy across robotic platforms.
• Incorporate large vision-language-action models to improve reasoning and safety.
• Own the full intelligence-to-action loop from sensor observations to policy decisions.
• Build the learning pipeline linking production outcomes to model evaluation and policy improvements.
• Drive architecture decisions across the autonomy stack and mentor the team.

Skills

Autonomy systems

Planning & decision-making

C++

Python

Production deployment

Education

MS or PhD in Robotics, Computer Science, Machine Learning, or related field

Tools

Real-time stacks

Simulation & testing

Job description

Knightscope is seeking a Staff Autonomy Engineer for its Sunnyvale headquarters to own the intelligence-to-action loop across deployed autonomous systems. You will drive perception, planning, and control, delivering end-to-end autonomy capabilities in safety-critical environments.

The role requires deep expertise in planning, learning-based methods, and real-time decision making, with a track record of shipping production-grade robotics software and mentoring engineers.