Humanoid Robotics Software and Research Engineer

FieldAI

Boston (MA)

On-site

USD 120,000 - 220,000

Full time

14 days+

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Job summary

FieldAI is seeking a Software/Research Engineer to help build learning systems powering humanoid robots. You will work across whole-body loco-manipulation, reinforcement learning, motion retargeting, and real-world deployment, closing the loop between research, simulation, and robots in the real world.

The role requires hands-on development and deployment experience with robotics systems, sim-to-real challenges, and scalable RL training pipelines.

Qualifications

  • BS/MS/PhD in Robotics, CS, or related field, or equivalent experience.
  • Experience with reinforcement learning or related learning-based approaches for robotics.
  • Strong understanding of robotics fundamentals such as kinematics, dynamics, control, and physical interaction.
  • Experience simulating and deploying robotic systems on hardware.

Responsibilities

  • Develop and train whole-body loco-manipulation policies for humanoid robots.
  • Deploy trained policies to physical humanoids and integrate them into production software.
  • Advance motion retargeting to map human motion to robot behaviors.
  • Reduce sim-to-real gap by improving simulators with real-world data.
  • Build automated evaluation and validation systems for policy transfer.
  • Improve scalability of robot-learning infrastructure for faster experimentation.

Skills

Reinforcement learning
Imitation learning
Generative models
Robotics fundamentals

Education

BS/MS/PhD in Robotics or CS or related field

Tools

NVIDIA Isaac Sim
Isaac Lab
Newton

Job description

FieldAI is seeking a Software/Research Engineer to help build learning systems powering humanoid robots. You will work across whole-body loco-manipulation, reinforcement learning, motion retargeting, and real-world deployment, closing the loop between research, simulation, and robots in the real world.

The role requires hands-on development and deployment experience with robotics systems, sim-to-real challenges, and scalable RL training pipelines.

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