Research Engineer, ML Infrastructure

Job summary

Responsibilities

Research moves at the speed of the infrastructure underneath it. Every training run, evaluation loop, and experimental iteration depends on systems that are fast, reliable, and built to scale. This role exists to make sure nothing in the stack becomes the bottleneck that slows down the frontier.

• Distributed Training Infrastructure: Build and own the systems that run large-scale training jobs reliably across GPU clusters.
• Scaling Agent Rollouts: Own the infrastructure that runs hundreds of thousands of concurrent coding agent rollouts in VM sandboxes, from high-fidelity environment design to the…
• Performance Optimization: Profile and improve training throughput end to end. Identify bottlenecks across data loading, communication overhead, memory utilization, and compute…
• Experiment Orchestration and Tooling: Design and maintain the systems researchers use to launch, track, and analyze experiments.
• Data Pipeline Engineering: Build high-throughput, reliable data pipelines for training and evaluation. Ensure data quality, reproducibility, and efficiency at the scale our…

Tech stack

• Python
• C++
• PyTorch