Job description

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

Requirements

Must-haves

About Knightscope
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.
7+ years shipping autonomy, planning, or decision-making systems to production in robotics, autonomous vehicles, or safety-critical platforms.
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.

Nice-to-haves

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.

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Keywords to mirror

aimachine learningmlpython

Platform: bamboohr
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off