OpenAI는 ML Framework Engineer를 채용합니다. Python 기반의 강력한 소프트웨어 엔지니어링 역량을 바탕으로, 대규모 모델 학습을 위한 분산 시스템 최적화 및 성능 프로파일링을 담당합니다. 연구원들과 협업하여 차세대 모델 개발을 가속화하고, 고성능 학습 프레임워크를 구축하는 핵심적인 역할을 수행하게 됩니다.
Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve.
Our work focuses on three pillars: high-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement; performant, high-uptime, fault-tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability); and distributed process management for long-lived, job-specific and user-provided processes.
We integrate proven large-scale capabilities into a composable, developer-facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model-stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products).
As a Training: ML Framework Engineer, you will work on improving the training throughput for our internal training framework, while enabling researchers to experiment with new ideas. This requires good engineering (for example designing, implementing, and optimizing state-of-the-art AI models), writing bug-free machine learning code (surprisingly difficult!), and acquiring deep knowledge of the performance of supercomputers. In all the projects this role pursues, the ultimate goal is to push the field forward.
We’re looking for people who love optimizing performance, understanding distributed systems, and who cannot stand having bugs in their code. Since our training framework is used for large runs with massive numbers of GPUs, performance improvements here will have a large impact.
This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
Apply the latest techniques in our internal training framework to achieve impressive hardware efficiency for our training runs
Profile and optimize our training framework
Work with researchers to enable them to develop the next generation of models
Have run small scale ML experiments
Love figuring out how systems work and continuously come up with ideas for how to make them faster while minimizing complexity and maintenance burden
Have strong software engineering skills and are proficient in Python
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