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

Netflix • Los Angeles, CA

2d

About The Position

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.
The Team
The Studio Media Algorithms team is at the forefront of algorithmic innovation to enhance and support the creation of Netflix’s entertainment content, including games. In this role, you will be embedded within this team while collaborating very closely with a specialized Games Studio R&D team. This incubation-style team is chartered to lead our investments in building new kinds of games leveraging emerging technologies to support our creators and reach player audiences in new ways.
The Role
We are looking for a Machine Learning Engineer with a focus on MLOps, deployment, and performance optimization to help bridge the gap between research and production in the gaming space. You will work cross-functionally with games technical directors, designers, and scientists to ensure that novel AI-driven game concepts can be deployed efficiently across a variety of hardware environments.

Requirements

• MLOps & Deployment Expertise: Proven experience with model registries, containerization, and building end-to-end CI/CD pipelines for machine learning. Experience productionizing ML models in the cloud (e.g., AWS and SageMaker endpoints), including scaling, monitoring, and working closely with platform/infra teams.
• Hardware Profiling & Acceleration: Experience in profiling and optimizing ML inference on GPUs, with knowledge of CUDA-based runtimes and tools (e.g., Nsight, cuDNN, TensorRT, ONNX Runtime).
• Compiler & Runtime Knowledge: Familiarity with graph compiler optimization and tools like MLIR or LLVM.
• Framework Proficiency: Extensive experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
• Strong Software Engineering: Ability to develop high-quality, maintainable code and integrate complex algorithmic solutions into production systems.
• Passion for Games: A strong interest in how technology enables joy and innovation in the video game industry.

Nice To Haves

• Hands-on experience deploying ML models on edge, such as iOS or Android devices, including model optimization and hardware-aware inference.
• Experience in game development and familiarity with game engines (e.g., Unity, Unreal).
• Experience in model distillation, pruning, or other model compression techniques.

Responsibilities

• Build and maintain MLOps pipelines: Develop robust CI/CD for ML, model registries, and automated deployment workflows to support rapid iteration.
• Optimize for performance: Profile and benchmark models across cloud GPUs and edge devices (e.g., Nsight, PyTorch Profiler) to identify bottlenecks and implement hardware acceleration.
• Scale deployment: Design and implement model deployment strategies for both Cloud and Edge environments, ensuring efficient, low-latency execution in game runtimes.
• Enhance model efficiency: Apply precision tuning and quantization techniques to meet latency, cost, and memory constraints without significant quality loss.
• Collaborate on integration: Work with game engineers to integrate ML models into game engine pipelines and APIs.

Benefits

• Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed