Senior Research Engineer, Humanoid Manipulation
Confirmed on the employer's own hiring board on Oct 30, 2026. First seen by Alion on Sep 2, 2026. FieldAI scores B on the Alion truth index.
We are seeking a Research Engineer to join our manipulation efforts. In this role, you will work at the intersection of robotics research and applied engineering, building, training, testing, and refining large-scale learned manipulation models and capabilities that accelerate autonomous control and loco-manipulation on humanoid robots. You will collaborate closely with research scientists, engineers, and product partners to design novel manipulation strategies and deliver systems that directly feed into FieldAI’s robot learning pipelines.
You will also play a key role in advancing robotics foundation models designed to be generalizable across embodiments, with an initial focus on humanoid platforms. The work will emphasize combinations of learned and physically-grounded models, alongside the data and training systems required to scale them. This role is about pushing the frontier of manipulation research while ensuring that breakthroughs translate into practical, scalable autonomy in real-world environments.
What You’ll Get To Do
Advance Humanoid Manipulation Research and Development
Design, implement, and evaluate learning-based manipulation models and strategies for humanoid robots across a wide range of tasks.
Drive projects from early concepts through on-robot testing and deployment.
Develop loco-manipulation capabilities that integrate perception, control, and planning.
Contribute to foundation models for manipulation, working on model architecture, data collection, large-scale training pipelines, and deployment infrastructure.
Ensure model development supports generalization and transfer across diverse robotic platforms.
Collaborate with research scientists to integrate large-scale manipulation data into learning pipelines powering foundation models.
Train and evaluate manipulation models using imitation learning, reinforcement learning, and other training approaches.
Translate research ideas into reliable robotic systems that operate in real-world conditions.
Ensure systems are robust, reproducible, and aligned with data collection and learning objectives.
Develop experimental infrastructure to support rapid iteration, large-scale training, and evaluation.
Partner with mechanical and electrical engineers on hardware integration and system bring-up.
Work closely with field teams to refine interfaces and improve manipulation performance.
Act as a connective layer between autonomy research and applied robotics engineering.
Prototype quickly, run experiments on hardware, and validate results in the field.
Balance exploratory research with concrete deliverables that support near-term goals.
Debug complex system-level issues spanning software, hardware, data, training infrastructure, and learning.
Drive Robotics Foundation Model Development
Build Systems That Bridge Research and Deployment
Collaborate Across Disciplines
Rapidly Iterate and Deliver
What You Have
Bachelor’s, Master’s, or PhD in Robotics, Computer Science, Mechanical Engineering, or a related field.
2+ years of hands-on experience in robotic manipulation and/or robot learning in academic or industry settings.
Strong foundation in robot kinematics, dynamics, and control, with a focus on manipulation.
Strong background in learning-based manipulation, with an emphasis on reinforcement learning and imitation learning, and experience training modern deep-learning models.
Experience using frameworks such as PyTorch and building reproducible model-training and evaluation pipelines.
Proven experience implementing and evaluating robotic systems on real hardware.
Ability to work effectively in fast-paced, highly collaborative environments.
Curiosity, ownership, and the confidence to challenge assumptions and propose new approaches.
The Extras That Set You Apart
Experience working with humanoid robots or multi-fingered robotic hands.
Experience training large-scale robot foundation models.
Experience with reinforcement-learning fine-tuning, offline RL, and related policy training methods.
Familiarity with large-scale data collection, spanning simulation, web-scale data, human-in-the-loop data, and autonomously-collected data.
Publications or open-source contributions in top robotics or machine-learning venues.
Experience with large-scale robotics data collection and dataset management.
Strong interest in bridging cutting-edge research with field-ready robotic systems.