ML Systems Engineer – Training & Inference

About the role

You will build the systems that let our researchers train, evaluate, and serve large foundation models reliably at scale. This role sits at the intersection of model research and infrastructure, with a focus on accelerator-based (TPU) training and inference, performance, reproducibility, and researcher velocity. You will own the paved path that turns expensive, long-running model

What you'll do

• Build and improve the paved path for distributed training, evaluation, experiment tracking
• Checkpoint management, model release, and inference on Cloud TPUs
• Operate long-running ML workloads with strong observability, failure detection, automated
• Recovery, and practical operational tooling
• Profile and remove bottlenecks across compute, HBM and host memory, data input

What you need

• 3–6 yrs of experience
• Python
• Machine learning
• Java
• C++
• Kubernetes
• Go
• Any graduate

Good to have

• LLMs
• ETL
• Incident response
• Root cause analysis
• GCP
• MLOps