Machine Learning Engineer, Training Optimization

Role Overview

• You’ll design, implement, and optimize large-scale machine learning systems for training
• You’ll improve all aspects of performance, including GPU utilization, communication overhead, and memory efficiency.
• You’ll partner with research and modeling teams to align systems with algorithmic needs.
• You’ll evaluate and apply best practices for distributed training using industry-leading frameworks.
• You’ll dive deep into low-level optimization, including custom CUDA or Triton kernels.
• You’ll debug, profile, and fine-tune training workflows to unlock new levels of scalability.

Requirements

• Strong background in LLMs, multimodal AI, or diffusion models.
• Proficiency in Python.
• Familiarity with a system programming language (e.g. C++ or Rust) is a plus.
• Deep knowledge of PyTorch or JAX as well as libraries such as Megatron-LM, NeMo, or DeepSpeed.
• Familiarity with common optimization techniques such as FSDP/ZeRO, gradient checkpointing, or low-precision data types.
• Hands-on experience writing custom GPU kernels in CUDA or Triton.
• Excellent communication and problem-solving skills, incl. full proficiency in English.

Tech Stack

• Python
• PyTorch
• Rust

Benefits

• Employees can work remotely