Développeur.se - opérations liées à l’apprentissage automatique / MLOps Developer
About The Position
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. 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 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.
Requirements
- 8+ years of engineering experience
- 4+ years building and operating ML systems in production
- Expertise in Python
- Comfort reading and modifying framework-level code — PyTorch, and an inference server such as vLLM, SGLang or TensorRT-LLM
- Demonstrated model or inference optimization work
- Hands-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 equivalent
- Working knowledge of AWS compute and storage as they apply to ML workloads — EC2 GPU instances, S3 and its performance tiers, EKS
- Experience with containerization and CI/CD for ML artifacts and a reproducibility instinct
Responsibilities
- Optimize how our models train and serve.
- Build the streaming data path from lake to GPU.
- Make our data formats a deliberate choice.
- 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.
- Land the training and experiment telemetry into one place researchers and planners trust.
Benefits
- paid time off (3 weeks per year to start)
- 80 hours per year of sick time
- 16 paid company holidays per year
- 10 weeks paid time off to bond with baby
- medical/dental/vision insurance
- life insurance
- disability insurance
- 401(k)
- bonus and other incentive programs
- 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
- retirement plan
- complimentary games
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
