Lead Humanoid Robotics Research Scientist

FieldAI; Boston, Northern (MA, KY); Hybrid; USD 150,000 - 300,000; Full time

Benefits offered by this job

Restaurant d'entreprise

Indemnités de stage/alternance

Job summary

FieldAI in Boston is seeking a Research Scientist to advance humanoid manipulation and foundation models. You will design experiments, develop scalable learning methods, and validate on real robots, collaborating with research engineers and systems teams to translate advances into deployed capabilities.

Applicants should have a PhD in a related field, a strong publication record, and deep expertise in RL, IL, multimodal learning, and PyTorch.

Qualifications

• PhD in Robotics, CS, ML, EE, ME, or related field.
• Strong research record in robot learning, robotic manipulation, reinforcement learning, imitation learning, multimodal learning, or a related area.
• Ability to lead research projects from an initial technical question through rigorous experimental validation.
• Deep expertise in at least one area, such as foundation models, VLAs, generative policies, reinforcement learning, imitation learning, or multimodal foundation models.
• Experience training and evaluating modern deep-learning models using PyTorch.
• Strong understanding of robotic manipulation, including relevant aspects of kinematics, dynamics, control, perception, and planning.
• Experience deploying and evaluating learning-based methods on physical robotic systems.
• Strong experimental judgment, including the ability to design informative ablations, baselines, metrics, and evaluation protocols.
• A track record of publications or equivalent research impact in leading robotics or machine-learning venues.
• Ability to communicate research ideas clearly and collaborate effectively across research, engineering, systems, and product teams.

Responsibilities

• Lead high-impact humanoid manipulation research projects.
• Develop model architectures, learning objectives, action representations, and training methods for large-scale robot learning.
• Establish strong baselines, evaluation protocols, and benchmarks for manipulation performance and generalization.
• Research large-scale VLAs and other multimodal behavior models that connect perception, language, reasoning, and continuous robot action.
• Develop imitation-learning and reinforcement-learning methods for improving robustness, precision, dexterity, and long-horizon task performance.
• Investigate model adaptation, temporal abstraction, memory, uncertainty, data efficiency, and compositional skill learning.
• Study how model performance scales with architecture, data quantity, data quality, task diversity, and embodiment diversity.
• Develop methods for transferring capabilities across robots while accounting for embodiment-specific sensing and control constraints.
• Lead research projects from initial hypothesis through large-scale training, deployment, and validation on physical humanoid robots.
• Design real-world experiments that expose model limitations and measure generalization under realistic environmental variation.
• Analyze failures at data, representations, policy, perception, and control levels.
• Use deployments to guide new research questions and model improvements.
• Demonstrate learned capabilities across dexterous manipulation, bimanual coordination, and loco-manipulation tasks.

Skills

Robot learning

Reinforcement learning

Imitation learning

Multimodal learning

Foundation models

PyTorch

Robotic manipulation

Experimental design

Publications

Cross-team collaboration

Education

PhD in Robotics or related field

Tools

PyTorch

Job description

FieldAI in Boston is seeking a Research Scientist to advance humanoid manipulation and foundation models. You will design experiments, develop scalable learning methods, and validate on real robots, collaborating with research engineers and systems teams to translate advances into deployed capabilities.

Applicants should have a PhD in a related field, a strong publication record, and deep expertise in RL, IL, multimodal learning, and PyTorch.