> Markdown version of [/jobs/ext/4006624-research-software-engineer-humanoid-whole-body-learning](https://www.wearedevelopers.com/jobs/ext/4006624-research-software-engineer-humanoid-whole-body-learning). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Research / Software Engineer - Humanoid Whole Body Learning

- **Company:** Level-FieldAI LLC
- **Location:** Pittsburgh, PA, United States
- **Contract:** Permanent contract
- **Skills:** Kinematics, Machine Learning, Robotic Automation Software, Reinforcement Learning, Generative AI, Isaac Sim, Information Technology
- **Published:** October 29, 2026
- **Apply:** https://www.thejobnetwork.com/job/7cf61fb1-ed0e-4dfe-887b-cb89a7925ce2/research-software-engineer-humanoid-whole-body-learning

## About the Role

* BS, MS, or PhD in Robotics, Computer Science, Machine Learning, Engineering, or a related field, or equivalent experience.
 * Experience with reinforcement learning, imitation learning, generative models, or other learning-based approaches for robotics.
 * Strong understanding of robotics fundamentals such as kinematics, dynamics, control, and physical interaction.
 * Experience developing and evaluating robotic systems in simulation and/or on physical hardware.
 * Ability to move comfortably between research experimentation and production-quality engineering.

What Will Set You Apart

 * Hands-on experience with humanoid robots.
 * Experience with whole-body loco-manipulation.
 * Experience with GPU-accelerated simulation frameworks such as NVIDIA Isaac Sim, Isaac Lab, and/or Newton.
 * Experience with motion retargeting.
 * Experience taking learned robot behaviors from simulation to real hardware.
 * Experience building scalable RL training, evaluation, and/or automated robot-testing infrastructure.

## Description

* Develop and train whole-body loco-manipulation policies for humanoid robots.
 * Deploy trained policies to physical humanoids and integrate them into our production software stack.
 * Advance our motion retargeting pipeline, transforming human motion into physically plausible, robot-executable behaviors that interact with diverse environments and objects.
 * Reduce the sim-to-real gap by improving simulation fidelity and closing the real-to-sim loop, using real-world robot data to calibrate and refine our simulators.
 * Build automated evaluation and validation systems that make it faster and more reliable to move policies from simulation onto physical robots.
 * Improve the performance and scalability of our robot-learning infrastructure, enabling faster experimentation and policy iteration.
 * Work closely with researchers and engineers across perception, learning, simulation, and hardware to turn new ideas into deployed robot capabilities.