ML Engineering Intern

About this role

About the role: - Build training and inference systems that push the boundaries of modern machine learning - Engage with cutting-edge architectures spanning Vision Language Models, multi-modal systems, and generative AI technologies - Take ownership of model implementations by mastering the underlying mathematics rather than relying on framework abstractions alone - Optimize models to run efficiently across both edge devices and cloud infrastructure - Ground your work in fundamental principles rather than treating libraries as black boxes What you'll work on: - Designing and refining data pipelines that feed state-of-the-art models through their training cycles - Writing performant inference code that bridges research and production - Implementing complex neural network architectures from mathematical first principles - Debugging and optimizing systems for speed, memory usage, and accuracy What we're looking for: - Expert-level proficiency in Python across all problem domains - Deep understanding of computer science fundamentals including multi-threading, memory management, and distributed systems concepts - A mindset of extreme ownership and accountability for code quality and system performance - Willingness to learn the mathematics behind your models rather than treat them as utilities Selection process: - Coding challenge requiring you to solve a complex algorithmic problem (Medium/Hard LeetCode level) in Python - Technical interview where you'll implement core ML components from scratch using only Python and NumPy—such as building a Transformer block or Convolution layer—to demonstrate your grasp of both math and engineering