Research Scientist Intern, AI Infra (PhD)

Meta • Menlo Park, CA

6d • $7,650 - $12,667

About The Position

Meta is seeking AI Research interns to join its Video Recommendations team within Facebook, focusing on the training and inference infrastructure behind video recommendations across Reels, long videos, and Live streams. The intern 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. The role sits at the intersection of machine learning, systems, and GPU computing and involves collaboration with Research Scientists and Software Engineers to achieve measurable improvements in training efficiency and inference performance. Internships last 12 to 24 weeks, with various start dates throughout the year.

Requirements

• Currently has or is in the process of obtaining a Ph.D. in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, or a relevant technical field.
• Must obtain work authorization in the 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 machine learning systems, performance optimization, GPU computing, distributed systems, or AI agents.

Nice To Haves

• Intent to return to the degree program after completion of the internship or 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 evaluating 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 with 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.

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 by using profiling traces and validated experiment results to improve infrastructure performance and 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 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: Ph.D. or professional degree