Pioneer Humanoid Manipulation Engineer

FieldAI; Boston (MA); On-site; USD 150,000 - 300,000; Full time

Job summary

FieldAI is seeking a Research Engineer to advance humanoid manipulation and robotics foundation models. You will design and test learning-based manipulation strategies, train scalable models, and ensure generalization across diverse humanoid platforms while collaborating with engineers for real-world deployment.

The role emphasizes bridging cutting-edge research with practical robotic systems, using imitation and reinforcement learning, and building robust training pipelines across simulation

Qualifications

• Bachelor’s, Master’s, or PhD in Robotics, CS, ME, or related field.
• 2+ years hands-on experience in robotic manipulation or robot learning.
• Strong foundation in robot kinematics, dynamics and control.

Responsibilities

• Design, implement, and evaluate learning-based manipulation models for humanoid robots.
• Drive projects from concepts to on-robot testing and deployment.
• Develop loco-manipulation capabilities integrating perception, control, and planning.
• Contribute to foundation models for manipulation and large-scale training pipelines.
• Bridge research ideas to reliable robotic systems for real-world conditions.
• Collaborate with engineers on hardware integration and field interfaces.
• Prototype rapidly, run experiments on hardware, and iterate toward near-term goals.

Skills

Robotics

Machine Learning

PyTorch

Reinforcement Learning

Imitation Learning

Robot Manipulation

Control

Education

Bachelor’s, Master’s, or PhD in Robotics, Computer Science, or related field

Tools

PyTorch

Training Pipelines

Job description

FieldAI is seeking a Research Engineer to advance humanoid manipulation and robotics foundation models. You will design and test learning-based manipulation strategies, train scalable models, and ensure generalization across diverse humanoid platforms while collaborating with engineers for real-world deployment.

The role emphasizes bridging cutting-edge research with practical robotic systems, using imitation and reinforcement learning, and building robust training pipelines across simulation