Research / Software Engineer - Humanoid Whole Body Learning

FieldAI is transforming how robots interact with the real world. Our growing R&D team is based in Boston, where we develop risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use.

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.

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.

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.

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.

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