Research Scientist Intern, AI Infra (PhD)

Meta; Menlo Park (CA); On-site; USD 55,000 - 83,000; Full time

Job summary

Meta is seeking AI Research interns to join our Video Recommendations team in Menlo Park, California, focusing on training and inference infrastructure behind video recommendations. You will work on scalable AI systems with researchers and engineers to improve training efficiency and inference performance. The program lasts 12 to 24 weeks with multiple start dates.

Applicants should be pursuing a Ph.D. in a relevant field and be eligible to work in the United States for the duration of the

Qualifications

• Pursuing a PhD in Computer Science, Computer Engineering, AI, ML, or related field.
• Experience programming in Python, C++, or related language.
• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• Familiarity with ML training or inference systems and GPU computing.

Responsibilities

• Pursue a scoped research project on a workload bottleneck, researching and building AI agents to analyze and optimize infrastructure.
• Collaborate with Research Scientists and Software Engineers to translate research into measurable gains in training efficiency and inference performance.

Skills

Python

C++

PyTorch

TensorFlow

CUDA

GPU computing

ML systems

Distribution systems

Research

Education

PhD in CS/CE/AI/ML or related field

Tools

NVIDIA Nsight

CUDA toolkit

Triton

ML compilers

Job description

Meta is seeking AI Research interns to join our Video Recommendations team within Facebook, focusing on the training and inference infrastructure behind video recommendations. The team helps people discover engaging, high-quality content across Reels, long videos, and Live streams.

In this role, you will pursue a scoped research project on a selected workload or performance bottleneck, researching and building AI agents that analyze, optimize, and iteratively improve recommendation infrastructure. You will work at the intersection of machine learning, systems, and GPU computing, collaborating with Research Scientists and Software Engineers to turn research into measurable gains in training efficiency and inference performance.

Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year. Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, or a relevant technical field Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment.

• Experience programming in Python, C++, or another related language
• Experience with deep learning frameworks such as PyTorch or TensorFlow
• Familiarity with machine learning and the fundamentals of training or inference systems
• Research or project experience in at least one of the following areas: machine learning systems, performance optimization, GPU computing, distributed systems, or AI agents
• Intent to return to the degree program after the completion of the internship/co-op
• Research achievements demonstrated by publications, grants, fellowships, patents, or open source contributions in machine learning or systems, including venues such as MLSys, ASPLOS, OSDI, NeurIPS, ICML, ICLR, or KDD
• Experience developing LLM agents that use tools, coding agents, reinforcement learning, or feedback-driven optimization loops, including evaluation of agent performance and recursive self-improvement
• Experience with CUDA, Triton, GPU kernel optimization, or machine learning compilers, including memory optimization, kernel fusion, and automated tuning
• Experience profiling training or inference workloads and analyzing traces using tools such as PyTorch Profiler, NVIDIA Nsight Systems, Nsight Compute, or equivalent tools
• Experience with distributed training, inference serving, or recommendation infrastructure, including batching, caching, quantization, communication, or data pipeline optimization
• Experience with recommendation systems, sequence models, or large-scale video workloads
• Demonstrated software development and collaboration experience through technical internships, research projects, work experience, or contributions to open source machine learning and systems tools