Electronic Arts · Montréal
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At EA, we believe games are powerful because they bring together multiple ways people engage: play, watch, create, and connect. And increasingly, the biggest entertainment platforms aren't just places to consume content - they're places where communities build.
Creator-made content is already a proven part of EA's history - from community creation tools in Battlefield to The Gallery in The Sims 4. We believe new creative technologies and tools will expand how players engage with and contribute to our experiences, supported by thoughtful product design, safety systems, and global reach. Our focus is on enabling more players to participate in creative expression by making creation easier, safer, and more rewarding.
As a Senior ML Data Engineer, you will own the layer between raw game capture and research-ready datasets. You will define how multimodal game data is structured, enriched, evaluated, discovered, and delivered, so researchers can build and assess new machine learning systems with confidence.
You will lead Research Embedded Data Engineers across research pillars and work with central infrastructure and data teams to turn repeated research requirements into shared capabilities. Your focus will be the architecture and utility of the datasets rather than ownership of generic storage, compute, or data-lake services.
This is a hybrid role, working three days per week in Redwood City, Montreal, or Vancouver.
You will report to the Lead Technical Director.
Responsibilities:
You will define canonical schemas and data contracts for synchronized game capture, assets, metadata, annotations, and derived research data.
You will lead the technical strategy for turning certified raw captures into reproducible, research-ready datasets.
You will guide Research Embedded Data Engineers across research pillars, align priorities, and consolidate repeated requirements into shared solutions.
You will partner with researchers to define dataset requirements, acceptance criteria, sampling strategies, and measures of downstream utility.
You will design systems for dataset discovery, versioning, lineage, composition, and repeatable train, validation, and evaluation splits.
You will develop automated methods for semantic enrichment, indexing, labeling, deduplication, coverage analysis, and quality assessment.
You will establish metrics that identify gaps, bias, corruption, and low-value repetition across multimodal datasets.
You will define interfaces with central infrastructure and data platforms, ensuring research requirements are met without duplicating foundational services.
You will provide technical leadership through architecture reviews, documentation, mentoring, and hands-on implementation of critical data capabilities.
Qualifications:
12+ years of experience in data engineering, machine learning infrastructure, or applied machine learning, including ownership of large-scale data systems.
Experience designing datasets for computer vision, reinforcement learning, world models, robotics, or other multimodal machine learning applications.
Expertise with Python and modern data-processing frameworks.
Experience working with large collections of video, images, time-series telemetry, structured metadata, or 3D data.
Experience with schema design, dataset versioning, lineage, reproducibility, and data-quality measurement.
Experience developing search, indexing, sampling, labeling, or enrichment workflows for machine learning data.
Experience translating research objectives into concrete data requirements and reusable technical systems.
Experience leading engineers through technical direction, architecture decisions, mentoring, and prioritization.
Experience collaborating with research, game-engine, infrastructure, security, legal, and product partners.
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