Graduate Research Internship in Robot Learning

Job Description

About Company: Field AI

Field AI is a pioneering company dedicated to transforming how robots interact with the real world. They specialize in building risk-aware, reliable, and field-ready AI systems designed to tackle the most intricate challenges in robotics, thereby unleashing the full potential of embodied intelligence.

The company distinguishes itself by moving beyond conventional data-driven or purely transformer-based approaches, charting a novel course in the field. Their solutions are already globally deployed, delivering tangible real-world results and undergoing rapid improvements through practical field applications.

Field AI’s core innovation lies in their Field Foundational Models™, which are setting new benchmarks in crucial areas like perception, planning, localization, and manipulation. A key emphasis is placed on ensuring their approach is both explainable and safe for deployment.

The company prides itself on a world-class team characterized by creativity, resilience, and bold thinking. With a decade-long track record of deploying successful solutions in the field, winning segments of the DARPA challenge, and bringing expertise from leading organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, Field AI is well-positioned to achieve its ambitious goals.

Field AI is committed to being at the forefront of the next robotics revolution. They seek innovators who dare to go beyond conventional methods, challenge the status quo, and bring interdisciplinary expertise. The team is diverse, comprising top AI talent, exceptional software developers, engineers, product designers, field deployment experts, and communicators.

Headquartered in Irvine, Southern California, the company also boasts US-based and global teammates. Field AI fosters an inclusive environment, celebrating diversity and ensuring that candidates and employees are evaluated solely based on merit, qualifications, and performance, without discrimination.

Job Description: Graduate Research Internship in Robot Learning

Field AI is offering a Graduate Research Internship in Robot Learning, specifically tailored for MS and PhD students who are eager to contribute to the advancement of embodied intelligence.

As an intern, you will have the unique opportunity to work intimately with Field AI’s researchers and engineers. Your primary focus will be to investigate novel methods for skill learning, foundation models in robotics, and large-scale training pipelines.

This internship provides a comprehensive experience, allowing you to develop research ideas, design experiments, and directly test your work on real robots. This bridges cutting-edge AI research with practical, real-world field deployment. Interns will also have the chance to contribute significantly to publications, open-source software, and collaborative projects that aim to advance the broader robotics research community.

What You’ll Get To Do (Responsibilities):

• Conduct Research in Robot Skill Learning:
• Investigate and develop algorithms for learning transferable skills across various robot embodiments.
• Explore and apply approaches from reinforcement learning (RL), imitation learning, and multimodal foundation models.
• Advance Foundation Models for Robotics:
• Adapt Visual-Language Models (VLMs) and Large Language Models (LLMs) for specific robotics applications, focusing on perception, reasoning, and control.
• Work on methods for effectively grounding language and vision models within real-world robot tasks.
• Large-Scale Training and Experimentation:
• Design and execute large-scale training pipelines, primarily utilizing PyTorch and distributed systems.
• Conduct experiments involving sim-to-real transfer, domain adaptation techniques, and the exploration of scaling laws relevant to robot learning.
• Deploy and Validate on Real Robots:
• Test and rigorously validate your research ideas on diverse real robot platforms, including manipulators, mobile robots, and others.
• Address challenging field robotics problems such as traversability analysis, locomotion, and dexterous manipulation.
• Collaborate and Publish:
• Partner closely with Field AI’s research team to propel projects forward, aiming for real-world impact.
• Contribute to publications in top-tier academic venues, including CoRL, ICRA, NeurIPS, ICML, CVPR, among others, where appropriate.

What You Have (Required Qualifications):

• Currently enrolled as an MS or PhD student in Robotics, Computer Science, AI/ML, or a closely related field.
• Demonstrated research experience in areas such as robot learning, reinforcement learning, imitation learning, or foundation models.
• Strong proficiency with Python and widely used machine learning frameworks, with PyTorch strongly preferred.
• A solid background in machine learning fundamentals and experimental design.
• The ability to analyze research results rigorously and iterate quickly on new ideas.
• A genuine passion for robotics and embodied intelligence.

The Extras That Set You Apart (Preferred Qualifications):

• Published research in leading robotics, AI/ML, or related fields (e.g., CoRL, ICRA, IROS, NeurIPS, ICML, CVPR).
• Experience with distributed training, large-scale machine learning systems, or high-performance computing (HPC).
• Hands-on experience with real robot platforms and research specifically focused on sim-to-real transfer.
• Familiarity with ROS/ROS2 or other common robot middleware.
• Contributions to open-source projects in robotics or AI.
• A background in perception (e.g., 3D vision, mapping, traversability analysis) or dexterous manipulation.

Compensation and Benefits:

• The company offers a competitive salary range of $70,000 – $300,000 annually.
• The final base pay is determined by considering an individual’s background, experience, geographic location, and specific job-related knowledge and skills.
• While on-site collaboration is valued, Field AI is open to exploring hybrid or remote work options.