Senior Machine Learning Engineer - Reinforcement Learning
Path RoboticsLast week
- Where you can work from
- Remote — United States onlyUnited States
- Salary
- Salary not disclosedThe employer did not publish a range. We do not estimate one.
- Job type
- Full-time
- Experience
- Senior
Skills
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Job description
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 Sr.ML 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. This role can be located in our Columbus, Ohio Headquarters or Remote.
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 It 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.
If you require a reasonable accommodation to participate in the application process or any part of the hiring process, please contact . We are committed to providing equal access and will work with qualified individuals to ensure a fair and accessible hiring experience. We will respond to your request within 48 hours.
Originally posted on Himalayas
Job availability & source
Still listed on the employer's own board at our last check, 21 hours ago
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- Source
- Himalayas
- Posted by the employer
- 13 Oct 2026
- First seen by us
- 13 Oct 2026
- Last checked
- 25 Oct 2026
via Himalayas — view on source
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