Research Scientist Intern, AI Infra (PhD)

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.

Responsibilities

Research and develop AI agents that optimize training and inference for video recommendation models, including distributed execution, data pipelines, model compilation, and serving.
Investigate recursive self-improvement for video recommendation infrastructure: use profiling traces and validated experiment results to improve infrastructure performance and the agents' subsequent optimization decisions.
Build GPU kernel agents that generate and tune CUDA or Triton kernels, explore memory access and kernel fusion, and verify numerical correctness and performance.
Develop agentic trace analyzers that correlate CPU/GPU timelines, distributed traces, and runtime telemetry to diagnose bottlenecks and explain root causes in training and inference.
Build agentic profilers for both training and inference that select profiling tools, collect targeted measurements, and recommend optimizations for compute, memory, communication, and scheduling.
Design reproducible benchmarks and evaluation methods for optimization agents, measuring training throughput, inference latency and throughput, GPU utilization, memory use, and cost while preserving model quality.
Collaborate with Research Scientists, Software Engineers, and infrastructure partners to scope experiments, build research prototypes, and translate validated findings into practical improvements.
Communicate research plans, progress, and results through technical reports, reusable tools, and publications where appropriate.

Qualifications

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