Staff ML Engineer, Autonomy & Planning

AboutKnightscope

Knightscope (NASDAQ: KSCP) is a security technology company building the nation's first Autonomous Security Force - autonomous machines, AI-driven software, and elite security professionals operating as one integrated managed service. The Company is on a mission to make the United States of America the safest country in the world. One Team. One Force.

About the Role

The gap between a robot thatobservesand a robot that acts is the intelligence layer. We are looking for a Staff Autonomy Engineer to own thislayerend to end. You will define how ourrobot’s reasonabout their environment and respond, drawing on the latest advances in embodied AI, learned planning, and real-time decision making. This is not a role where you own one slice of a large stack. You will architect and ship the full intelligence-to-action loop on a deployed productionfleet andbuild the learning pipeline that makes it better with every mission.

Key Responsibilities

• Advance core AI and machine learning capabilities for embodied autonomy, spanning computer vision, learned planning, open-world generalization, and real-time decision making, across deployed robotic platforms.
• Push the boundaries of learning-based robotics by incorporating large vision-language-action models to improve reasoning, situational understanding, and explainability in safety-critical environments.
• Own the full intelligence-to-action loop: from sensor observations through entity reasoning, world-state modeling, to policy decisions that drive safe, bounded robot behavior.
• Build the closed-loop learning pipeline: production outcomes and operator feedback feed model evaluation and policy improvement withoutmodifyingsafety-critical boundaries.
• Drive technical architecture decisions across the autonomy stack and mentor engineers across the team.

Required Qualifications

• 7+ years shipping autonomy, planning, or decision-making systems to production in robotics, autonomous vehicles, or safety-critical platforms.
• Deepexpertisein one or more areas: behavior planning and policy execution, imitation learning or reinforcement learning for real-world robot control, end-to-end learned autonomy systems, or large-scale ML for real-time decision making.
• Hands-on experience integrating learned planning or policy models with classical control systems and deterministic safety constraints.
• Strong software engineering in C++ and Python; experiencewith real-time autonomy stacks.
• Demonstrated ability to take systems from researchprototypeto deployed productionplatform.
• Track recordof driving architecture decisions acrossperception, planning, andcontrolsteams.

Preferred Qualifications

• MS or PhD in Robotics, Computer Science, Machine Learning, or relatedfield.
• Experience with autonomous vehicles, deployed robotics, or embodied AI systems in production.
• Background in trajectory planning, behavior modeling, or autonomy policy design.
• Familiarity with VLA architectures, foundation models for robot control, or large-scale pre-trained policies adapted for real-world deployment.
• Experience building data flywheel or simulation-based training pipelines forproductionML systems.
• Familiarity with functional safety standards, ISO26262or SOTIF.

Compensation & Benefits

• Base Salary: $240,000 to $275,000
• Equity: Stock options
• Benefits: Medical, dental, vision, 401(k), paid time off
• Location Requirement: Full-time, on-site at Sunnyvale HQ