Machine Learning Engineer (L4/L5) - Emerging Game Technologies

Netflix

Los Gatos, California, United States of America
Base: $466,000.00 - $750,000.00; bonus/equity: not...
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Mlops pipeline development
Cloud gpu and edge device profiling
Pytorch or tensorflow framework proficiency
** Netflix is seeking a Machine Learning Engineer for their Studio Media Algorithms team, focusing on MLOps and performance optimization in gaming technologies. The role involves developing efficient deployment strategies for AI-driven game concepts and requires strong technical expertise in machine learning and a passion for the gaming industry. **

Job Summary

  • The role focuses on bridging the gap between research and production by building robust MLOps pipelines for emerging game technologies.
  • Candidates will optimize model performance across cloud GPUs and edge devices to ensure low-latency execution in game runtimes.
  • Netflix offers a unique compensation structure where employees choose their mix of salary versus stock options annually.

Matching Summary

Match Score: 75

** Netflix is seeking a Machine Learning Engineer for their Studio Media Algorithms team, focusing on MLOps and performance optimization in gaming technologies. The role involves developing efficient deployment strategies for AI-driven game concepts and requires strong technical expertise in machine learning and a passion for the gaming industry. **

Salary

Base: $466,000.00 - $750,000.00; Bonus/Equity: Not specified (salary vs stock choice); Benefits: Comprehensive health plans, 401(k) match, flexible time off

Skills & Requirements

Must-have

  • MLOps pipeline development
  • Cloud GPU and edge device profiling
  • PyTorch or TensorFlow framework proficiency
  • Model quantization and precision tuning
  • CI/CD for machine learning systems

Nice-to-have

  • Experience with Unity or Unreal Engine
  • iOS or Android edge deployment experience
  • Knowledge of MLIR or LLVM compilers
  • Passion for video game industry innovation
  • Model distillation and pruning techniques

Key Requirements

  • Proven experience productionizing ML models in cloud environments
  • Strong software engineering skills for production systems
  • Familiarity with CUDA-based runtimes and tools like TensorRT

Work Rights

Not specified

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