Description & RequirementsElectronic 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.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.« Pour visualiser la description de poste en français, veuillez sélectionner le français dans le menu déroulant au haut de la page. »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 current 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 an MLOps Engineer, you will work closely with researchers to optimize models, help shape the data they need for training, and instrument the system to measure and improve model performance.



Working in close partnership with the Lead Infrastructure Engineer and Research Leads, you will translate requirements into durable operational processes and ensure the fleet operates effectively.You will report to the Head of Data & Infrastructure.ResponsibilitiesYou will optimize how our models train and serve.You will build the streaming data path from lake to GPU.You will make our data formats a deliberate choice.You will instrument all of it including Per-job GPU utilization, queue depth and job success rate, cost per experiment, data-loader stall time, and serving latency percentiles.You will land the training and experiment telemetry into one place researchers and planners trust.Qualifications8+ years of engineering experience4+ years building and operating ML systems in productionExpertise in PythonComfort reading and modifying framework-level code — Py Torch, and an inference server such as v LLM, SGLang or Tensor RT-LLMDemonstrated model or inference optimization workHands‑on experience with distributed data processing for ML (including Ray, Spark, or equivalent) and columnar and ML‑native formats (such as Parquet, Arrow, Lance, or similar)Practical observability skills including Prometheus, Grafana, or equivalentWorking knowledge of AWS compute and storage as they apply to ML workloads — EC2 GPU instances, S3 and its performance tiers, EKSExperience with containerization and CI/CD for ML artifacts and a reproducibility instinctThis is a hybrid role, onsite three days a week, in Redwood City, Montreal, or Vancouver.We want to connect you with job opportunities that align with your interests, skills, and expertise. When you create an EA Careers

📌 Développeur.Se - Opérations Liées À L’apprentissage Automatique / Mlops Developer (Vancouver)
🏢 Electronic Arts
📍 Vancouver