Research Scientist Intern, Robotics Foundation Models, Agentic RL (PhD)

Summary:

Meta Reality Labs Research is a team of researchers and engineers pushing the frontier of AI and robotics. Our team is building the next generation of world action models (WAMs) and vision-language-action models (VLAs) for robotic manipulation. We are seeking a Research Scientist Intern to develop scalable offline reinforcement learning methods for post-training these models.Interns in this role will conduct fundamental and applied research to advance foundation models for robotics, with a focus on post-training. You will collaborate with research scientists and engineers to develop new algorithms, build and evaluate model capabilities, and contribute to the next generation of embodied AI.Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

Required Skills:

Research Scientist Intern, Robotics Foundation Models, Agentic RL (PhD) Responsibilities:

1. Develop agentic RL algorithms for post-training WAMs and VLAs using large, heterogeneous robot datasets.

2. Investigate agentic RSI, reward learning, value estimation, and policy optimization for generative action models, including approaches that use learned world models to support policy improvement.

3. Address challenges in learning from mixed-quality experience, including distribution shift, sparse rewards, and long-horizon credit assignment.

4. Build efficient training and data pipelines, and study how policy performance scales with dataset size, model capacity, and compute.

5. Design experiments and benchmarks to evaluate policy improvement and generalization across tasks and environments.

6. Collaborate with research scientists and engineers to evaluate learned policies in simulation and on real robots.

7. Communicate research findings and publish results at leading machine learning and robotics venues.

Minimum Qualifications:

Minimum Qualifications:

1. Research experience in reinforcement learning, including policy optimization, value learning, or offline/off-policy learning

2. Experience implementing and training deep learning models using Python and PyTorch or a comparable framework

3. Experience designing controlled experiments, analyzing results, and debugging machine learning algorithms

4. Interpersonal experience: cross-group and cross-culture collaboration

5. Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment

Preferred Qualifications:

Preferred Qualifications:

1. Intent to return to degree program after the completion of the internship/co-op

2. Experience developing offline RL methods, particularly for continuous control, large datasets, or learning from suboptimal experience

3. Experience training or post-training VLAs, WAMs, or other foundation models for embodied decision-making

4. Experience with diffusion or flow-based policies, action-sequence prediction, model-based RL, or action-conditioned world models

5. Hands-on experience with distributed training across multiple GPUs or nodes, including improving training throughput, memory efficiency, and stability

6. Experience evaluating learned policies on physical robots or robotics simulation benchmarks

7. Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, and/or accepted papers in top conferences or journals such as NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, CVPR, ICCV, ECCV, ACL, etc

Public Compensation:

$7,650/month to $12,667/month + benefits

Industry: Internet

Equal Opportunity:

Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.

Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at [email protected].