Principal ML Engineer

Electronic Arts creates superior entertainment experiences that inspire players and fans worldwide. Here, everyone is part of the story. 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. 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.

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, lineage, quality, coverage, acceptance criteria, and reproducible splits
• Build deterministic loaders and checkpoint-safe iteration that recover from worker or hardware failures and corrupt data
• Design reproducible dataset mixtures and balanced batches across modalities, resolutions, sequence lengths, titles, and capture conditions
• Benchmark and enforce throughput, data-wait time, step time, and accelerator utilization and stalls
• Diagnose bottlenecks and intermittent failures across storage, network I/O, CPU, memory, decoding, preprocessing, and accelerators
• 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, or equivalent stacks
• 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 with Ray Data, Mosaic Streaming, WebDataset, NVIDIA DALI, NVDEC, or equivalent technologies

Benefits:

• Comprehensive benefits package, including physical, emotional, financial, work-life, and collective well-being support
• Opportunities for growth and professional development