Machine Learning Engineer, Reinforcement Learning

Path Robotics • Columbus, OH

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About The Position

At Path Robotics, we’re building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use. Big, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together. As a Machine Learning Engineer focused on Reinforcement Learning, you will design, implement, and optimize RL algorithms that enable intelligent agents to operate in dynamic, unstructured environments. This role involves working closely with cross-functional teams to design, test, and deploy innovative solutions that improve the performance and capabilities of our robotic systems.

Requirements

• Master’s or PhD in Computer Science, Robotics, Machine Learning, or related field, or equivalent practical experience.
• Strong knowledge of reinforcement learning algorithms and theory.
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Experience with simulation environments (e.g., MuJoCo, Isaac Gym).
• Solid understanding of probability, statistics, and optimization.
• Experience with training and deploying ML models in production systems.

Responsibilities

• Design, implement, and evaluate RL algorithms for robotic control, motion planning, and adaptive behaviors in dynamic, unstructured environments.
• Develop and integrate RL policies with robot control systems, ensuring compatibility with hardware constraints and real-time requirements.
• Collaborate with perception teams to fuse RL with vision, depth, and sensor data for robust decision-making.
• Build and maintain sim-to-real pipelines, including domain randomization and transfer learning techniques.
• Conduct experiments on physical robots, including designing safety protocols and monitoring for unexpected behaviors.
• Leverage simulation environments (Isaac Gym, Gazebo, MuJoCo, PyBullet) for large-scale training before real-world validation.
• Continuously improve model efficiency to operate within compute and latency constraints on embedded robotic systems.

Benefits

• Daily free lunch to keep you fueled and connected with the team
• Flexible PTO so you can take the time you need, when you need it
• Comprehensive medical, dental, and vision coverage
• 6 weeks fully paid parental leave, plus an additional 6–8 weeks for birthing parents (12–14 weeks total)
• 401(k) retirement plan through Empower
• Generous employee referral bonuses—help us grow our team!

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 101-250 employees