Robotics Research, Humanoid Manipulation Intern (Summer 2027) | PhD at Field AI
Boston, MAInternshipsInternPosted about a month ago$40–$60 per hour
About the role
As published on Field AI's job board.
FieldAI is transforming how robots interact with the real world. Our growing R&D team is based in Boston, where we develop risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use.
We are offering a Fall 2026 internship focused on humanoid manipulation for PhD students interested in advancing embodied intelligence on humanoids. As a research intern, you will work at the intersection of robotics research and applied engineering, contributing to manipulation capabilities that directly support FieldAI’s autonomy and robot learning efforts while advancing the field of robotics through academic research with the goal of publishing a paper to a leading journal or conference.
What You Will Get To Do Advance Humanoid Manipulation Research
- Design, implement, and evaluate manipulation strategies for humanoid robots across diverse tasks.
- Explore loco-manipulation problems that integrate perception, planning, and control.
- Work towards super-human dexterity, developing systems that outperform human teleoperation.
Contribute to the Research Community
- Work towards publishing a paper in a leading robotics conference/journal
- Work closely with other researchers, both internal and external.
- Present your work at Field at robotic conferences
Contribute to Robotics Foundation Model Development
- Support data collection pipelines used to train robotics foundation models.
- Work with embodiment-agnostic representations to enable transfer across robot platforms.
- Collaborate with researchers to integrate manipulation data into scalable learning frameworks.
Collaborate Across Disciplines
- Work with teleoperators and field teams to refine interfaces and improve manipulation outcomes.
- Partner closely with researchers and engineers to align experiments with broader autonomy goals.
- Engage with mechanical and electrical engineers on hardware integration and system bring-up.
What You Have • Current PhD student in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, AI/ML, or a closely related field.
- Research experience in robotic manipulation, loco-manipulation, or related robotics domains.
- Strong foundation in robot kinematics, dynamics, and control.
- Proficiency in Python and/or C++, with experience using robotics or ML tooling.
- Experience designing experiments and evaluating results on robotic systems (simulation or hardware).
- Curiosity, initiative, and a strong interest in embodied intelligence and real-world robotics.
- Publications in leading robotics journals/conferences (ICRA, IROS, CORL, RA-L, T-RO, etc.)
The Extras That Set You Apart • Prior experience working with humanoid robots or dexterous robotic hands.
- Background in learning-based manipulation, including imitation learning or reinforcement learning.
- Hands-on experience running experiments on real robot hardware.
- Familiarity with ROS or ROS 2.
- Publications, preprints, or open-source contributions in robotics or AI.
- Interest in bridging cutting-edge research with practical, field-ready robotic systems.
Why Join FieldAI? FieldAI is tackling one of robotics’ hardest problems: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models™ advance perception, planning, localization, and manipulation with an emphasis on explainability and safety, so our systems can be trusted where it matters most. You will work alongside a world-class team that values creativity, resilience, and bold thinking. We bring a decade-long track record of real-world deployments, strong performance in DARPA challenges, and experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX. Our R&D organization is growing and anchored in Boston, with close collaboration across our teams in Southern California and with colleagues around the US and globally. Be Part of the Next Robotics Revolution Solving problems at this scale takes a team as unique as the mission. We are looking for people who push beyond conventional approaches, enjoy tackling tough and ambiguous questions, and bring interdisciplinary perspective. Our success depends on exceptional AI researchers and engineers, as well as strong software developers, product designers, field deployment experts, and communicators who can turn breakthroughs into real capability. We are headquartered in Irvine, Southern California, with teammates across the US and around the world. Join us to shape the future of embodied intelligence as part of a fun, close-knit team building systems that work in the real world. Equal Opportunity FieldAI celebrates diversity and is committed to creating an inclusive environment for all employees. Candidates and employees are evaluated based on merit, qualifications, and performance. We do not discriminate on the basis of race, color, religion, sex, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected status.
Before you apply
- Scan your resume against this posting to see which of its terms you are missing.
- Tailor your resume so the summary and bullets use the posting's own wording where it is true for you.
- Read the internship resume guide and the interview questions for the role.
- Apply on Field AI's site. Applications never pass through OneTwo Resume.
About Field AI
Develops embodied AI software that lets mobile robots operate autonomously in unstructured environments.
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