Develop robust CI/CD for ML, model registries, and automated deployment workflows to support rapid iteration.
Profile and benchmark models across cloud GPUs and edge devices to identify bottlenecks and implement hardware acceleration.
Design and implement model deployment strategies for both Cloud and Edge environments, ensuring efficient, low-latency execution in game runtimes.
Apply precision tuning and quantization techniques to meet latency, cost, and memory constraints without significant quality loss.
Work with game engineers to integrate ML models into game engine pipelines and APIs.

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.
Experience in profiling and optimizing ML inference on GPUs, with knowledge of CUDA-based runtimes and tools (e.g., Nsight, cuDNN, TensorRT, ONNX Runtime).
Familiarity with graph compiler optimization and tools like MLIR or LLVM.
Extensive experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
Ability to develop high-quality, maintainable code and integrate complex algorithmic solutions into production systems.
A strong interest in how technology enables joy and innovation in the video game industry.

Health Plans
Mental Health support
401(k) Retirement Plan with employer match
Stock Option Program
Disability Programs
Health Savings and Flexible Spending Accounts
Family-forming benefits
Life and Serious Injury Benefits
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

Machine Learning Engineer, Emerging Game Technologies

Key skills