Closed listing
Développeur.se sénior.e des données d’apprentissage automatique / Senior ML Data Developer
- Vancouver, BC, Canada
Full-time
This opening has closed.
The original listing is kept below for reference.
Job details
- Job type
- Full-time
- Closing date
- Experience
- 12+ years of experience in data engineering, machine learning infrastructure, or applied machine learning, including ownership of large-scale data systems.
- Posting language
- English
- Posted
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Search current openingsOriginal job description
Archived listing. The details below describe a past opening.
Electronic Arts creates next-level entertainment experiences that inspire players and fans around the world. Here, everyone is part of the story. Part of a community that connects across the globe. A place where creativity thrives, new perspectives are invited, and ideas matter. A team where everyone makes play happen.
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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.
- 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.
Pay Transparency - North America
Compensation And Benefits
The ranges listed below are what EA in good faith expects to pay applicants for this role in these locations at the time of this posting. If you reside in a different location, a recruiter will advise on the applicable range and benefits. Pay offered will be determined based on a number of relevant business and candidate factors (e.g. education, qualifications, certifications, experience, skills, geographic location, or business needs).
PAY RANGES
- British Columbia (depending on location e.g. Vancouver vs. Victoria) *$169,500 - $242,600 CAD
For Canada, we offer a package of benefits including vacation (3 weeks per year to start), 10 days per year of sick time, paid top-up to EI/QPIP benefits up to 100% of base salary when you welcome a new child (12 weeks for maternity, and 4 weeks for parental/adoption leave), extended health/dental/vision coverage, life insurance, disability insurance, retirement plan to regular full-time employees. Certain roles may also be eligible for bonus and other incentive programs.
About Electronic Arts
We’re proud to have an extensive portfolio of games and experiences, locations around the world, and opportunities across EA. We value adaptability, resilience, creativity, and curiosity. From leadership that brings out your potential, to creating space for learning and experimenting, we empower you to do great work and pursue opportunities for growth.
We adopt a holistic approach to our benefits programs, emphasizing physical, emotional, financial, career, and community wellness to support a balanced life. Our packages are tailored to meet local needs and may include healthcare coverage, mental well-being support, retirement savings, paid time off, family leaves, complimentary games, and more. We nurture environments where our teams can always bring their best to what they do.
Electronic Arts is an equal opportunity employer. All employment decisions are made without regard to race, color, national origin, ancestry, sex, gender, gender identity or expression, sexual orientation, age, genetic information, religion, disability, medical condition, pregnancy, marital status, family status, veteran status, or any other characteristic protected by law. We will also consider employment qualified applicants with criminal records in accordance with applicable law. EA also makes workplace accommodations for qualified individuals with disabilities as required by applicable law.
More about this job
Responsibilities
Define the architecture, schemas, and technical strategy for turning raw game captures into reproducible, research-ready multimodal datasets, including discovery, versioning, enrichment, evaluation, and quality measurement. Lead and mentor embedded data engineers, partner with researchers and platform teams, and build shared data capabilities that support machine learning research.
Requirements
Requires 12 or more years of experience in data engineering, machine learning infrastructure, or applied machine learning, including ownership of large-scale data systems. Candidates should have expertise in Python and modern data-processing frameworks, experience with multimodal datasets and reproducible data workflows, and a record of technical leadership and cross-functional collaboration.
Skills
- Data Engineering
- Machine Learning Infrastructure
- Python
- Multimodal Machine Learning
- Computer Vision
- Reinforcement Learning
- Dataset Design
- Schema Design
- Dataset Versioning
- Data Lineage
- Data Quality Measurement
- Search and Indexing
- Data Sampling
- Data Labeling
- Semantic Enrichment
- Technical Leadership
Benefits
- Vacation
- Sick Leave
- Paid Parental Leave
- Health Insurance
- Dental Insurance
- Vision Coverage
- Life Insurance
- Disability Insurance
- Retirement Plan
- Bonus Programs
- Incentive Programs
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Engineering
- Management & Leadership
Keywords
- Python
- Data Engineering
- Machine Learning
- Machine Learning Infrastructure
- Multimodal Data
- Computer Vision
- Reinforcement Learning
- World Models
- Robotics
- Game Capture
- Video Data
- Image Data
- Time-Series Telemetry
- 3D Data
- Data Processing Frameworks
- Canonical Schemas
- Data Contracts
- Dataset Versioning
- Data Lineage
- Reproducibility
- Dataset Discovery
- Indexing
- Sampling Strategies
- Data Labeling
- Semantic Enrichment
- Deduplication
- Coverage Analysis
- Data Quality Assessment
- Train-Validation-Evaluation Splits
- Research Data Systems