We are Genmo, a research lab dedicated to building open, state-of-the-art models for video generation towards unlocking the right brain of AGI. Join us in shaping the future of AI and pushing the boundaries of what's possible in video generation.

Role overview:

As a Deep Learning Performance Engineer at Genmo, you will play a critical role in optimizing the performance of our large generative AI models. Your expertise will ensure that our models run efficiently on clusters, leveraging advanced techniques and tools to enhance their performance. This role is perfect for someone with a deep understanding of deep learning performance bottlenecks, kernel optimization, and distributed training strategies.

Key responsibilities:

• Analyze and optimize the performance of massively parallel and distributed systems

• Implement and fine-tune distributed training strategies for multi-GPU and multi-node environments

• Implement high-performance CUDA, Triton, C++ and PyTorch code.

• Profile model performance and identify bottlenecks using tools like NVIDIA NSight Systems, PyTorch Profiler, and TensorFlow Profiler

• Develop and maintain benchmarking suites for continuous performance monitoring

Qualifications:

• Master's or PhD in Computer Science, Electrical Engineering, or a related field

• 5+ years of experience in optimizing deep learning models, preferably in a production environment

• Must have

  • Strong programming skills in Python and C++. Experience in training large models using Python & PyTorch and/or TensorFlow including their distributed training frameworks.

  • Proven track record of optimizing large-scale models (10B+ parameters)

  • Deep understanding of GPU architecture and CUDA programming

  • Experience in entire development pipeline from data processing, preparation & data loading to training and inference.

  • Experience optimizing and deploying inference workloads for throughput and latency across the stack (inputs, model inference, outputs, parallel processing etc.)

  • Demonstrated expertise in high-performance computing using NVIDIA Triton and CUDA

  • Demonstrated ability to significantly improve model inference and training speeds through low-level optimizations

• Ideal candidates will have:

  • Knowledge of distributed inference systems for handling high-volume workloads

  • Strong background in linear algebra, optimization, and machine learning algorithms

  • Experience with generative AI models (GANs, Diffusion Models, Transformers)

  • Knowledge of hardware-aware neural architecture design

  • Experience with high-performance computing (HPC) environments

  • Contributions to relevant open source projects or research publications

Genmo is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law. Genmo, Inc. is an E-Verify company and you may review the Notice of E-Verify Participation and the Right to Work posters in English and Spanish.