Member of Technical Staff, ML Optimization

About the role

We’re looking for a Senior ML Infrastructure Engineer to help us optimize our AI models for media generation. The ideal candidate for this role has extensive experience improving the performance of model training and inference, strong low-level understanding of GPU workloads, and thrives in fast-paced, high-ownership environment.

What you’ll do

• Optimize our state-of-the-art AI models for video generation, such as Gen-1 and Gen-2
• Build tooling to improve the efficiency and reliability of distributed training runs on Runway’s HPC cluster

What you’ll need

• 3+ years of experience in a role optimizing machine learning model inference and training on NVIDIA hardware
• Knowledge of Python, C/C++, CUDA, and extensive experience profiling GPU performance and distributed training runs
• Contributions to an ML framework (such as PyTorch), optimized runtimes for inference (such as TensorRT) or compilers (such as GCC)
• Strong communication, collaboration, and documentation skills

Runway strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on competitive market rates for our size, stage and industry, and salary is just one part of the overall compensation package we provide.

There are many factors that go into salary determinations, including relevant experience, skill level and qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.

Lastly, the provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range, which again, will be communicated to candidates.

Salary range: $300,000-$370,000