Research / Software Engineer - Humanoid Whole Body Learning

Job description

[FieldAI](https://www.fieldai.com/) 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. ### What You Will Get To Do - 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. ### What You Bring - 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.