Staff ML Engineer, Autonomy & Planning
At a glance
Senior Machine Learning Engineering role at knightscope. Sunnyvale · full-time · $240,000–$275,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build autonomy capabilities across vision, learned planning, generalization to unfamiliar situations, and real-time decisions on deployed robots
- Use vision-language-action models to strengthen robot reasoning, situational awareness, and explanations in safety-critical settings
- Turn sensor input into world-state models and policy choices that keep robot behavior safe and bounded
- Use mission results and operator feedback to evaluate models and improve policies while preserving safety limits
- Set autonomy architecture direction and mentor engineers across the team
What you bring
- At least seven years delivering autonomy, planning, or decision systems in production robotics, autonomous vehicles, or safety-critical platforms
- Strong depth in behavior planning, robot learning, learned autonomy, or real-time machine learning systems
- Practical experience connecting learned policies with classical controls and deterministic safety limits
- Strong C++ and Python skills, experience with real-time autonomy systems, and a record of taking prototypes into production
Who this fits
This senior staff role suits an engineer who can lead architecture across perception, planning, and controls while mentoring teammates. It is full-time and on-site at the Sunnyvale headquarters.
From the employer
About Knightscope
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 that observes and a robot that acts is the intelligence layer. We are looking for a Staff Autonomy Engineer to own this layer end to end. You will define how our robot’s reason about 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 production fleet and build 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 without modifying safety-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.
- Deep expertise in 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; experience with real-time autonomy stacks.
- Demonstrated ability to take systems from research prototype to deployed production platform.
- Track record of driving architecture decisions across perception, planning, and controls teams.
Preferred Qualifications
- MS or PhD in Robotics, Computer Science, Machine Learning, or related field.
- 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 for production ML systems.
- Familiarity with functional safety standards, ISO 26262 or 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