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# Reinforcement Learning Engineer

- **Company:** Person AI Inc.
- **Location:** United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Software Debugging, Python (Programming Language), Machine Learning, Reinforcement Learning, Pytorch, Deep Learning, Information Technology
- **Published:** June 23, 2026
- **Apply:** https://www.dice.com/job-detail/2040ed77-77c5-426a-9938-c9801efdfbe2

## About the Role

* BS, MS, or PhD in Robotics, Computer Science, Machine Learning, or a related field.
 * 2+ years of hands-on experience in reinforcement learning for robotic manipulation; exceptional entry level candidates from relevant research labs will be considered.
 * Demonstrated ability to read, understand, and implement ideas from recent robotics and machine learning research.
 * Hands-on experience training RL agents for robotic manipulation tasks, including reward shaping and policy evaluation.
 * Experience with sim-to-real transfer: domain randomization, physics tuning, or real-world policy validation on hardware.
 * Proficiency in Python and deep learning frameworks (PyTorch, JAX), along with RL libraries such as rsl_rl or skrl.
 * Experience preparing meshes and collision geometries for RL environments in simulators such as MuJoCo and/or Isaac Sim.

Bonus Qualifications:

 * Experience deploying RL-trained policies on physical robotic hands.
 * Experience with tactile sensors and integrating tactile feedback into learned grasp policies.
 * Experience with contact-rich manipulation and force/torque estimation.
 * Familiarity with other learning-based approaches such as behavior cloning, imitation learning, or diffusion-based policy methods.
 * Publications or project work at top-tier venues (CoRL, RSS, ICRA) on grasping or dexterous manipulation.
 * Experience in a humanoid robot startup environment.

## Description

* Train and iterate on reinforcement learning policies for complex grasping tasks including functional grasping, tool use, in-hand manipulation, and environment interaction.
 * Implement and refine sim-to-real transfer pipelines to bridge the gap between simulation and physical robotic hand performance.
 * Develop reward functions, curriculum strategies, and training environments in MuJoCo and Isaac Lab.
 * Run experiments on real robots alongside simulation, evaluating and debugging policy behavior on hardware.
 * Monitor, evaluate, and adapt state-of-the-art research in learning-based grasping to deploy on our humanoid platform.
 * Collaborate with the rest of the software team to deploy end-to-end grasping systems.
 * Benchmark and evaluate grasp policies across object diversity, clutter scenes, and real-world uncertainties.
 * Integrate tactile sensing and feedback into grasp policies for robust, force-aware manipulation.


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