Machine Learning Engineer - Electronic Arts [EA251110]

About Us!

Founded in 2011, Hatch Innovations is based in Vancouver and has an international team with over 80 people located across the globe. We’re creative thinkers who have a passion for technology and are constantly thinking of innovative solutions to help better serve your business and customers. Our clients include video game publishers such as Electronic Arts, Epic Games and Krafton.

EA - Project Air :

Project Air is a new kind of mobile platform — part creative stage, part storytelling tool, part social experience — powered by AI and real-time interaction. Imagine the creative freedom and discovery of TikTok, the emotional texture of Pixar, and the emergent storytelling of the Sims, fused into something entirely new.

We're building a space where people don't just watch or post — they create, perform, and co-invent new characters, stories, and worlds. It's expressive, emotional, and deeply interactive.

About the Role!

We are seeking a highly skilled Machine Learning Engineer focused on MLOps to bridge the gap between model research and real-world application. Your primary responsibility will be to design, build, and maintain the robust, scalable infrastructure and pipelines required to deploy, monitor, and manage machine learning models in production environments. If you excel at transforming prototypes into reliable, low-latency services used by millions, this role is for you.

You will :

• Production Deployment : Take cutting-edge models and prototypes developed by Data Scientists and Researchers and transform them into production-ready, high-availability services (e.g., APIs, microservices).
• ML Pipeline Development : Architect and build automated, scalable pipelines for the entire ML lifecycle, including data ingestion, feature engineering, model training, validation, testing, and deployment.
• Performance Optimization : Optimize models and serving infrastructure for low latency, high throughput, and efficient resource utilization at scale.
• MLOps & Observability : Implement comprehensive MLOps practices, including monitoring tools for model drift, data quality, and service health; build systems for automated retraining; and ensure robust logging and observability.
• Infrastructure : Work closely with engineering teams to leverage and integrate models into our core services and applications.
• Maintainability & Quality : Write clean, well-tested, and maintainable code, adhering to software engineering best practices and contributing to code reviews.

You Have : Core Experience

MLOps & Infrastructure

Nice to Haves :

How to Apply :

Job Types : Permanent, Full-time

Schedule : Monday to Friday

Pay :

$150K to $200k per year

We Offer :

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