ML Infra Engineer, Modeling

🏢 ОфисMiddleПолная занятость

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Наша компания

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.

Чем предстоит заниматься

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

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

Наши требования

Deep ML systems background (e.g., training compilers, runtime optimization, custom kernels)

Experience operating close to hardware (GPU/TPU performance tuning)

Background in robotics, multimodal models, or large-scale foundation models

Experience designing abstractions that balance researcher flexibility with system reliability

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records

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)

Ability to debug and optimize performance bottlenecks across the training stack

Strong cross-functional communication and ownership mindset

Physical Intelligence

San Francisco

ГрейдMiddle

ЗанятостьПолная занятость

РегионНе Россия

ФорматОфис

ИсточникСкрыто

Опубликовано3 дн назад

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