Research / Software Engineer, Humanoid Whole Body Learning
Required skills
- whole-body loco-manipulation
- reinforcement learning
- motion retargeting
- sim-to-real
- robotics
- kinematics
- dynamics
- control
- physical interaction
- production software stack
Nice to have
- humanoid robots
- NVIDIA Isaac Sim
- NVIDIA Isaac Lab
- Newton
- scalable RL training
- automated robot-testing infrastructure
Role summary
FieldAI is seeking a Research / Software Engineer to develop and deploy AI systems for humanoid robots. This role focuses on whole-body loco-manipulation, reinforcement learning, motion retargeting, and bridging the sim-to-real gap. You will turn cutting-edge research into reliable capabilities on physical robots, working across the full stack from simulation to deployment. The ideal candidate has a strong background in robotics, machine learning, and software engineering, with experience in simulation and/or physical hardware. Experience with humanoid robots and specific simulation frameworks is a plus.
FieldAI is seeking a Software/Research Engineer to help build the learning systems that power our next generation of humanoid robots. You'll work across whole-body loco-manipulation, reinforcement learning, motion retargeting, and real-world robot deployment, turning new research and technical developments into reliable capabilities on physical humanoids. This is a highly hands-on role for someone excited about closing the loop between research, simulation, and robots operating in the real world.
What You Will Get To Do
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Develop and train whole-body loco-manipulation policies for humanoid robots.
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Deploy trained policies to physical humanoids and integrate them into our production software stack.
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Advance our motion retargeting pipeline, transforming human motion into physically plausible, robot-executable behaviors that interact with diverse environments and objects.
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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.
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Build automated evaluation and validation systems that make it faster and more reliable to move policies from simulation onto physical robots.
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Improve the performance and scalability of our robot-learning infrastructure, enabling faster experimentation and policy iteration.
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Work closely with researchers and engineers across perception, learning, simulation, and hardware to turn new ideas into deployed robot capabilities.
What You Bring
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BS, MS, or PhD in Robotics, Computer Science, Machine Learning, Engineering, or a related field, or equivalent experience.
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Experience with reinforcement learning, imitation learning, generative models, or other learning-based approaches for robotics.
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Strong understanding of robotics fundamentals such as kinematics, dynamics, control, and physical interaction.
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Experience developing and evaluating robotic systems in simulation and/or on physical hardware.
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Ability to move comfortably between research experimentation and production-quality engineering.
What Will Set You Apart
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Hands-on experience with humanoid robots.
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Experience with whole-body loco-manipulation.
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Experience with GPU-accelerated simulation frameworks such as NVIDIA Isaac Sim, Isaac Lab, and/or Newton.
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Experience with motion retargeting.
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Experience taking learned robot behaviors from simulation to real hardware.
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Experience building scalable RL training, evaluation, and/or automated robot-testing infrastructure.
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