Research Engineer, Large-Scale Training

Highlights

• Turn efficient training research into robust production systems at scale.
• Optimize large-scale training infrastructure across compute and communication.
• Bring new open-source foundation models to customers quickly and reliably.

What they offer

• startup equity
• health insurance

Ideal candidate

An independent problem-solver who thrives on ambiguous infrastructure challenges and loves turning research ideas into reliable systems that power real customer workloads.

Requirements

• Demonstrated ability to independently take ambiguous performance or infrastructure problems from investigation through deployment.
• Strong programming skills in Python and PyTorch, with an emphasis on writing efficient, maintainable code.
• Hands-on experience training or fine-tuning large neural networks in multi-GPU or multi-node environments.
• Solid understanding of ML systems fundamentals, including GPU architecture, mixed-precision training, and distributed training paradigms such as data, tensor, pipeline, or expert parallelism.
• Strong communication skills and the ability to collaborate effectively with both researchers and engineers.
• Passion for staying current with advances in AI research and applying them to real-world systems.
• Excitement about translating cutting-edge research into production systems that deliver customer impact.