ML Engineering Intern

Batch: 2026/2027/2028.

• 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

• 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

• 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

• Coding challenge requiring you to solve a complex algorithmic problem (Medium/Hard Leet Code level) in Python
• Technical interview where you'll implement core ML components from scratch using only Python and Num Py—such as building a Transformer block or Convolution layer—to demonstrate your grasp of both math and engineering