1.77 Robotics Research AI Engineer: Robot Learning

Position Overview

Field AI is transforming how robots interact with the real world by building risk-aware, reliable, and field-ready AI systems. As a Robotics Research AI Engineer (Robot Learning), you will push the frontier of embodied intelligence by designing and deploying cutting-edge learning algorithms that enable robots to acquire new skills, integrate visual‐language models (VLMs), large language models (LLMs), and control policies, and develop large-scale training pipelines. This role sits at the intersection of research and engineering, directly impacting robotics applications in traversability, dexterous manipulation, and more.

Key Responsibilities

• Develop Robot Skill Learning Methods – design and train algorithms that enable robots to acquire generalizable skills through reinforcement learning, imitation learning, and integration of foundation models.
• Advance Robotics Foundation Models – leverage and adapt VLMs and LLMs to address perception, reasoning, and action planning challenges in robotics.
• Large-Scale Training and Deployment – build and optimize distributed training pipelines using PyTorch and ensure successful deployment onto real robots.
• Real-World Robotic Applications – tackle core challenges in traversability, mobile robotics, and manipulation while collaborating with hardware engineers in unstructured environments.
• Research and Publication – conduct innovative research, publish in top venues, and contribute to the broader robotics research community.

Required Qualifications

• Strong research background (PhD, MS, or equivalent industry research experience) in Robotics, AI/ML, or related fields.
• Proficiency in robot learning methods such as reinforcement learning, imitation learning, and representation learning.
• Experience with PyTorch and modern machine learning training frameworks.
• Hands-on experience working with real robots, including mobile platforms or manipulators.
• Expertise in large-scale distributed training and optimization.
• Ability to translate research insights into practical, field-deployable systems.
• Excellent problem-solving skills and the ability to thrive in interdisciplinary teams.

Preferred Qualifications

• Publications in top-tier conferences/journals (e.g., CoRL, ICRA, IROS, NeurIPS, ICML, CVPR).
• Experience deploying VLMs/LLMs within robotics pipelines.
• Background in 3D vision, mapping, or traversability analysis.
• Experience in sim-to-real transfer strategies for manipulation and locomotion.
• Familiarity with modern robot middleware (such as ROS2 or Isaac).
• Strong software engineering practices including CI/CD, testing, and scalable infrastructure.
• Contributions to open-source robotics or machine learning frameworks.

Compensation and Benefits

• Compensation: Our salary range is between $70,000 and $300,000 annual; final salary will depend on experience, geographic location, and relevant expertise.
• Flexible Work Arrangements: Options include hybrid, on-site, or remote work.
• Opportunity to work with a world-class team boasting experience from organizations like DeepMind, NASA JPL, Boston Dynamics, and more.
• Join a diverse and inclusive environment that values creativity, bold thinking, and innovation.