Impactful Research Intern: Feed Recommendations

Meta; Bellevue (WA); On-site; USD 52,000 - 62,000; Part time

Job summary

Meta seeks Research Interns to join the Feed Recommendation team, tackling practical ML challenges at scale. You will frame real problems, develop innovative solutions, and work with researchers and engineers to advance recommender systems.

Internship durations range 12–24 weeks with multiple start dates. Ideal candidates are pursuing a PhD in CS/AI/NLP, have experience with Python/C++, and familiarity with PyTorch or TensorFlow.

Qualifications

• Pursuing or holding a PhD in CS/AI/NLP/related field.
• Work authorization for country of employment at hire and during internship.
• Proficient in Python, C++, Java or similar languages.
• Experience with PyTorch or TensorFlow.
• Experience building ML/NLP systems.
• Knowledge of transformers and large language models.
• Publications or grants in AI conferences desirable.
• Research or project experience in RL, LLM pre-training, diffusion models in rec systems.
• Software development experience via internships or OSS.
• Ability to work cross-functionally in fast-paced teams.
• Identify research opportunities and iterate with data-driven analysis.

Responsibilities

• Frame practical, high-impact challenges in recommendation into rigorous ML problems.
• Contribute to building innovative ML solutions under guidance from researchers and engineers.
• Collaborate with research scientists and software engineers on projects.
• Work on generative models and deep learning architectures relevant to recommender systems.

Skills

Python

C++

Deep learning

Transformers

Research experience

Cross-functional collaboration

ML/NLP projects

Education

Ph.D. in CS/AI/NLP/related field

Tools

PyTorch

TensorFlow

Git

Linux

Job description

Meta seeks Research Interns to join the Feed Recommendation team, tackling practical ML challenges at scale. You will frame real problems, develop innovative solutions, and work with researchers and engineers to advance recommender systems.

Internship durations range 12–24 weeks with multiple start dates. Ideal candidates are pursuing a PhD in CS/AI/NLP, have experience with Python/C++, and familiarity with PyTorch or TensorFlow.