Research Scientist Intern, AI Infra (PhD)

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.

Minimum 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

Preferred Qualifications

• 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

Compensation

• $7,650/month - $12,667/month; Country: US

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