AI/ML Research & Engineering Internship

About the Role

You’ll work on deploying and benchmarking open-source AI models, identifying performance bottlenecks, and testing optimization techniques—with an initial focus on image and video generation.

Responsibilities

• Deploy and benchmark open-source models across different hardware and software configurations.

• Measure inference latency, throughput, memory usage, hardware utilization, and cost.

• Explore batching, quantization, memory optimization, and other techniques to improve inference efficiency.

• Build reusable tools for performance monitoring and reproducible experiments.

• Document findings and evaluate performance improvements alongside output quality.

Minimum qualifications

• Pursuing a Phd or Master’s degree  in Computer Science, AI/ML, Electrical Engineering, Mathematics, or a related field.

• Strong Python skills and familiarity with PyTorch, Linux, and Git.

• A solid understanding of machine learning fundamentals.

• An analytical mindset, strong debugging skills, and the ability to work independently.

• Curiosity about how AI systems work beyond simply calling an API.

Preferred qualifications

• Experience with diffusion models, transformers, or Hugging Face.

• Exposure to GPU profiling, CUDA, or model serving.

• Personal projects, open-source contributions, or research experience demonstrating hands-on ability.