Description
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.
EA SPORTS™ is one of the most iconic brands in entertainment – connecting hundreds of millions around the world to the sports they love through a portfolio of industry-leading video games.
As one of the largest sports entertainment platforms in the world, EA SPORTS™ FC is redefining football with genre-leading interactive experiences, connecting a global community of fans to The World's Game through innovation and unrivaled authenticity.
With more opportunity than ever to design, innovate, and create new, immersive experiences that bring joy, inclusivity, and connection to fans everywhere, we invite you to join our passionate and dynamic team as we pioneer the future of football fandom.
Reporting to the Senior Data Science Manager, we are seeking a full-stack Machine Learning Engineer to operationalize, deploy, and scale high-impact machine learning solutions and end-to-end pipelines that power personalized, in-game experiences. As a key member of our multidisciplinary team, you will act as the bridge between technical engineering and strategic business objectives, ensuring robust software integration for our machine learning initiatives. The ideal candidate thrives in a dynamic environment, balancing applied technical execution with cross-team collaboration. You will be responsible for implementing resilient data architectures, maintaining scalable infrastructure, and collaborating with stakeholders to translate requirements into high-performance, production-ready systems.
Your Responsibilities:
- Deploy and maintain end-to-end Machine Learning pipelines to ensure robust data delivery and optimal model performance.
- Deploy Machine Learning models directly on target devices like gaming consoles and PC, optimized for specific platform constraints.
- Execute Large Language Model deployment and optimization for generative content and immersive in-game systems.
- Share technical knowledge by engaging with game teams to develop and ship high-impact features.
- Promote Machine Learning best practices through presentations and interactive demonstrations to elevate the team's craft.
- Stay abreast of latest ML advancements and prototype new application opportunities within the FC franchise.
Your Qualifications:
- Possess a BS in Computer Science, Mathematics, or a related field, or equivalent professional engineering experience.
- Demonstrate experience with Large Language Model deployment, fine-tuning, and retrieval-augmented generation techniques.
- Exhibit strong computer programming fundamentals with proficiency in Python and C++ or Java.
- Apply hands-on experience with Databricks, Trino, and Apache Airflow to manage production data and model workflows.
- Provide a proven record of building, deploying, and maintaining Machine Learning applications within productized software.
- Maintain experience deploying models on edge devices across the entire Machine Learning lifecycle.
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) $104,500 - $142,800 CAD
Pay is just one part of the overall compensation at EA.
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.