MTS, Research Engineer

What You'll Do

• Conduct Open-Ended Research: Explore new model architectures, training objectives, and optimization techniques. Formulate hypotheses, design experiments, and iterate quickly based on empirical results.
• Reproduce and Extend State-of-the-Art: Implement and reproduce results from recent machine learning papers. Identify bottlenecks, propose improvements, and scale these methods to larger datasets and models.
• Build and Scale Training Infrastructure: Design, implement, and maintain high-performance, distributed machine learning systems. Optimize training loops, data loaders, and communication overhead across large GPU clusters.
• Bridge Science and Engineering: Translate abstract mathematical concepts and research ideas into robust, bug-free, and efficient code.
• Collaborate Cross-Functionally: Work closely with Research Scientists to unblock their experiments by providing tooling, optimizing code, and co-designing experiments that are hardware-aware.

Requirements

• Strong programming skills (Python, C++, or Rust) and a commitment to writing clean, maintainable code.
• Deep practical knowledge of machine learning frameworks (PyTorch, JAX, or TensorFlow).
• Experience working with large distributed systems and parallel computing (e.g., CUDA, NCCL, MPI).
• A strong foundation in linear algebra, calculus, probability, and statistics.
• A proven track record of implementing complex deep learning algorithms from scratch.

Nice to Have

• A Master’s or PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related field (or equivalent industry experience).
• Experience with low-level GPU programming (CUDA/Triton) or hardware co-design.
• Familiarity with the challenges of training Large Language Models (LLMs).
• Familiarity with the challenges of inference, and OSS inference engines such as SGLang and vLLM.