Senior Machine Learning Engineer, Reinforcement Learning

AI overview

Design and optimize reinforcement learning algorithms for robotic systems, integrating advanced technologies for robust decision-making in dynamic environments.

Build the Path Forward

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 Senior 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.

What You’ll Do

  • 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.

Who You Are

  • Master’s or PhD in Computer Science, Robotics, Machine Learning, or related field, or equivalent practical experience.
  • Experience developing and deploying reinforcement learning algorithms on real-world systems.
  • 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.

Why You’ll Love Working Here

  • 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!

Who We Are

At Path Robotics we love coming to work to solve interesting and tough challenges but also because our ideas are welcomed and valued. We encourage unique thinking and are dedicated to creating a diverse and inclusive environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

Perks & Benefits Extracted with AI

  • Free Meals & Snacks: Daily free lunch to keep you fueled and connected with the team
  • Health Insurance: Comprehensive medical, dental, and vision coverage
  • Employee Referral Bonuses: Generous employee referral bonuses—help us grow our team!
  • Paid Parental Leave: 6 weeks fully paid parental leave, plus an additional 6–8 weeks for birthing parents (12–14 weeks total)

Path Robotics creates truly autonomous robots for manufacturing, eliminating the need for skilled welders or robot programmers and allowing humans to focus on creativity.

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