# Principal ML Engineer

**Company**: Electronic Arts
**Location**: Redwood City, California
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology
**Ticker**: EA
**Wikidata**: https://www.wikidata.org/wiki/Q173941

**Apply**: https://jobs.ea.com/zh_CN/careers/JobDetail/Principal-ML-Engineer/216087?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_7a49819b-633

## Description

Electronic Arts is seeking a Principal ML Engineer to own systems that prepare and deliver multimodal game data for large-scale model training. The successful candidate will architect and build shared training input pipelines, define schemas and data contracts, and optimize data throughput and accelerator utilization.

Responsibilities:

- Architect and build shared training input pipelines from certified game capture to accelerators

- Define schemas, data contracts, encodings, compression, and standards for dataset versioning and quality

- Build deterministic loaders and checkpoint-safe iteration for failure recovery

- Design reproducible dataset mixtures and balanced batches across modalities and resolutions

- Benchmark and enforce throughput, data-wait time, and accelerator utilization

- Diagnose bottlenecks and intermittent failures across storage, network I/O, CPU, memory, and accelerators

- Integrate compilers, runtimes, optimized kernels, and hardware-accelerated media processing

- Lead training-pipeline architecture reviews and mentor engineers

Qualifications:

- 20+ years of development experience, including owning production systems for distributed-training input

- Expertise building custom PyTorch training and ingestion systems or adapting TorchTitan, JAX/XLA with MaxText, Megatron Core/NeMo

- Expertise in FSDP2/HSDP, tensor/context/pipeline parallelism, mixed-precision training, and optimization on accelerator platforms

- Pre-training experience and understanding of how model architecture, data representation, batching, and orchestration affect training-system design and efficiency

- Expertise building large-scale multimodal streaming or hardware-accelerated ingestion with Ray Data, Mosaic Streaming, WebDataset, NVIDIA DALI, NVDEC

- Deep knowledge of sharding, sampling, packing, caching, prefetching, deterministic data mixing, checkpoint-safe iteration, and elastic restart

## Skills

### Required
- PyTorch
- TorchTitan
- JAX/XLA
- FSDP2/HSDP
- tensor/context/pipeline parallelism
- mixed-precision training
- Ray Data
- Mosaic Streaming
- WebDataset
- NVIDIA DALI
- NVDEC

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Source: [Apply at jobs.ea.com](https://jobs.ea.com/zh_CN/careers/JobDetail/Principal-ML-Engineer/216087?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)