# Principal ML Engineer

**Company**: Electronic Arts
**Location**: Vancouver, British Columbia
**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/es_ES/careers/JobDetail/Principal-ML-Engineer/216087?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_97fa8abb-a3c

## Description

Electronic Arts creates incredible entertainment experiences that inspire players and fans worldwide. Here, everyone is part of the story, part of a community that connects people globally. As a Principal ML Engineer, Training Pipelines, you will own the systems that prepare and deliver multimodal game data for large-scale model training.

You will architect and build shared training input pipelines from certified game capture to accelerators, covering storage layout, decoding, sharding, sampling, packing, batching, caching, prefetching, and host-to-device delivery across multiple training frameworks.

Your responsibilities will initially focus on hands-on individual contribution and technical leadership, with potential to take on people-management responsibilities as the team grows. You will partner with researchers and central data and compute teams to turn evolving model requirements into reusable training capabilities.

Key responsibilities:

- Architect and build shared training input pipelines

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

- Build deterministic loaders and checkpoint-safe iteration

- Design reproducible dataset mixtures and balanced batches

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

- Diagnose bottlenecks and intermittent failures

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

- Lead training-pipeline architecture reviews, mentor engineers, and align priorities with research leads

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 one or more accelerator platforms

- Pre-training experience and an 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

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

Benefits:

- Comprehensive benefit packages, including physical, emotional, financial, professional, and community well-being support

- Personalized packages to meet local needs, including medical insurance, mental wellness support, pension plans, paid time off, family leaves, free games, and more

## Skills

### Required
- PyTorch
- TorchTitan
- JAX/XLA
- MaxText
- Megatron Core/NeMo
- 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/es_ES/careers/JobDetail/Principal-ML-Engineer/216087?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)