Builds and owns distributed training infrastructure, experiment orchestration, data pipelines, and performance optimizations for large-scale AI research on GPU clusters. Requires deep systems expertise, Python/C++/PyTorch proficiency, and ML understanding to accelerate frontier research.
Salary not listed
On-siteDevOps / SRE
About the role
Responsibilities
Build and own distributed training infrastructure for large-scale GPU clusters, including job launchers, checkpointing, recovery, fault tolerance, and monitoring.
Own infrastructure for scaling agent rollouts in VM sandboxes at RL training scales.
Profile and optimize training throughput: data loading, communication, memory, compute efficiency to improve step time and MFU.
Design experiment orchestration and tooling to launch, track, and analyze experiments.
Build high-throughput, reliable data pipelines for training and evaluation.
Debug and resolve training failures across GPUs, networking, numerics, and data.
Implement and optimize parallelism strategies: data, tensor, pipeline, sequence.
Anticipate research needs and build scaling infrastructure proactively.
Requirements
Deep experience building/operating distributed training systems for large models.
Strong systems engineering: distributed systems, networking, storage, performance reasoning.
Proficiency in Python, C++; systems-level PyTorch or equivalent.
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