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# Staff Robot Intelligence & Integration Engineer

- **Company:** Knightscope, Inc.
- **Location:** Sunnyvale, CA, United States
- **Experience:** Expert
- **Salary:** $240,000.0 - $275,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, C++ (Programming Language), Python (Programming Language), Machine Learning, Language Modeling, Software Engineering, Systems Integration, Reinforcement Learning, Model Validation, Information Technology, Machine Learning Operations, Multiplatform
- **Published:** November 5, 2026
- **Apply:** https://www.careerbuilder.com/job-details/staff-robot-intelligence-integration-engineer-sunnyvale-ca--6c53cecf-b2ab-48c7-adc6-77771e8202fc

## About the Role

System Integration (SI), SLICE (Simulation Language with Integrated Circuit Emphasis), Vehicle Fleets, Artificial Intelligence (AI), Sales Pipeline, Machine Learning, Computer Vision, Multiplatform/Cross-Platform, Robotics, Modeling Languages, 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.

## Description

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.