FieldAI is seeking a Software/Research Engineer to help build the learning systems powering our next generation of humanoid robots. This on-site role is based in Boston, MA, and sits at the intersection of research, simulation, and real-world deployment, covering whole-body loco-manipulation, reinforcement learning, motion retargeting, and production software integration.

What you will 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 the motion retargeting pipeline to transform human motion into robot-executable behaviors. - Reduce the sim-to-real gap by improving simulation fidelity and calibrating simulators with real-world robot data. - Build automated evaluation and validation systems to move policies from simulation to hardware. - Improve the performance and scalability of our robot-learning infrastructure to enable faster experimentation and policy iteration. - Collaborate with researchers and engineers across perception, learning, simulation, and hardware to turn ideas into deployed 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 (kinematics, dynamics, control, physical interaction). - Experience developing and evaluating robo

FieldAI is seeking a Software/Research Engineer to help build the learning systems powering our next generation of humanoid robots. This on-site role is based in Boston, MA, and sits at the intersection of research, simulation, and real-world deployment, covering whole-body loco-manipulation, reinforcement learning, motion retargeting, and production software integration.

What you will 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 the motion retargeting pipeline to transform human motion into robot-executable behaviors. - Reduce the sim-to-real gap by improving simulation fidelity and calibrating simulators with real-world robot data. - Build automated evaluation and validation systems to move policies from simulation to hardware. - Improve the performance and scalability of our robot-learning infrastructure to enable faster experimentation and policy iteration. - Collaborate with researchers and engineers across perception, learning, simulation, and hardware to turn ideas into deployed 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 (kinematics, dynamics, control, physical interaction). - Experience developing and evaluating robo