ML Infra Engineer, Modeling

Job summary

Responsibilities

In this role you will help scale and optimize our training systems and core model code. You’ll own critical infrastructure for large-scale training, from managing GPU/TPU compute and job orchestration to building reusable and efficient JAX training pipelines.

• Own training/inference infrastructure: Design, implement, and maintain systems for large-scale model training, including scheduling, job management, checkpointing, and…
• Scale distributed training: Work with researchers to scale JAX-based training across TPU and GPU clusters with minimal friction.
• Optimize performance: Profile and improve memory usage, device utilization, throughput, and distributed synchronization.
• Enable rapid iteration: Build abstractions for launching, monitoring, debugging, and reproducing experiments.
• Partner with researchers: Translate research needs into infra capabilities and guide best practices for training at scale.

What they're looking for

• Strong software engineering fundamentals and experience building ML training infrastructure or internal platforms.
• Hands-on large-scale training experience in JAX (preferred), PyTorch.
• Familiarity with distributed training, multi-host setups, data loaders, and evaluation pipelines.
• Experience managing training workloads on cloud platforms (e.g., SLURM, Kubernetes, GCP TPU/GKE, AWS).
• Ability to debug and optimize performance bottlenecks across the training stack.

Tech stack

• JAX
• PyTorch
• Kubernetes
• GCP
• AWS