ML Infra Engineer

The ML Infrastructure team supports and accelerates PI’s core modeling efforts by building the systems that make large-scale training reliable, reproducible, and fast. The team works closely with research, data, and platform engineers to ensure models can scale from prototype to production-grade training runs.

- Own training/inference infrastructure: Design, implement, and maintain systems for large-scale model training, including scheduling, job management, checkpointing, and metrics/logging.

- 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.

- Manage compute resources: Ensure efficient allocation and utilization of cloud-based GPU/TPU compute while controlling cost.

- Partner with researchers: Translate research needs into infra capabilities and guide best practices for training at scale.

- Contribute to core training code: Evolve JAX model and training code to support new architectures, modalities, and evaluation metrics.

- 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).