Senior Machine Learning Engineer – Reinforcement Learning

Path Robotics

Sr. ML Engineer designing and optimizing RL algorithms for robotic systems.

Posted 7/13/2026full-timeRemote • Ohio • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in designing and implementing Reinforcement Learning algorithms for robotic control and motion planning, with a strong focus on real-time integration and simulation environments. Proficient in Python and deep learning frameworks, with a solid foundation in probability, statistics, and optimization for effective model deployment.

Highest-signal resume keywords

Reinforcement Learning AlgorithmsPython ProgrammingDeep Learning FrameworksSimulation EnvironmentsModel Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement LearningRobotic ControlMotion PlanningDomain RandomizationTransfer LearningProbabilityStatisticsOptimizationModel EfficiencyEmbedded Systems

Tools & Technologies

Isaac GymGazeboMuJoCoPyBulletReal-Time Systems

Industry Keywords

RoboticsMachine LearningComputer ScienceDynamic EnvironmentsSafety Protocols

Tech Stack

Tools & technologies

PythonPyTorchTensorflow

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

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!