Robotics Research Engineer – Reality Labs Research
| Company | Meta |
|---|---|
| Location | Redmond, WA, USA |
| Salary | $56.25 – $173000 |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s, PhD |
| Experience Level | Junior, Mid Level |
Requirements
- Currently has, or is in the process of obtaining a Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
- 2+ years of research or industry experience in robotics-related fields
- 3+ years of experience coding with Python, using deep learning frameworks (e.g., PyTorch), and software engineering best practices (version control, code reviews)
- Experience working with robotics-oriented frameworks like ROS or other real-world robotic control frameworks
- Experience bringing-up and debugging prototype/scientific software-hardware systems (e.g., robotics platforms, multi-camera systems, human sensing systems)
- Experience collaborating and communicating cross-functionally
Responsibilities
- Define and implement data collection protocols for collecting robot and human data and collaborate cross-functionally to collect quality data that will be used for robotic policy training
- Build, bring-up, and maintain prototype software and hardware systems for robot and/or human data collections
- Develop data pipelines that load, validate, preprocess, and clean up data to enable ML/CV training and evaluation
- Implement, integrate, and test ML models and control policies on real-time robotic manipulation systems. Evaluate Sim2Real gaps
Preferred Qualifications
- Masters or Ph.D. in Robotics, Computer Science, Electrical Engineering, relevant technical field, or equivalent practical experience
- Experience in robotics research, especially dexterous manipulation, robotic policy, and control theory
- Experience designing data collection protocols, developing data pipelines, and constructing high quality machine learning datasets
- Experience in computer vision, imitation learning, reinforcement learning, vision-language-action (VLA) models, multimodal representation learning, self-supervised learning, or other advanced ML/CV techniques