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.