Full-Time Senior Deep Learning Performance Software Engineer

We are looking for senior engineers who are mindful of performance analysis and optimization to help us squeeze every last clock cycle out of Deep Learning training, inference and NVIDIA AI Services. We are working across all layers of the hardware/software stack, from GPU architecture to Deep Learning Framework, to achieve peak performance. This role offers an opportunity to directly impact the hardware and software roadmap in a fast-growing company that leads the AI revolution.  Join the team building software used by the entire world. Work with world class software engineers to implement blazingly fast SOTA deep learning models that help understanding the end-to-end performance of NVIDIA’s DL software and hardware stack. Work on most powerful, enterprise-grade GPU clusters capable of hundreds of Peta FLOPS and on unreleased hardware before anyone in the world.

What you’ll be doing:

• Implement deep learning models from multiple data domains (CV, NLP/LLMs, ASR, TTS, RecSys and others) in multiple DL frameworks (PyT, JAX, TF2, DGL and others)

• Implement and test new SW features (Graph Compilation, reduced precision training) that use the most recent HW functionalities.

• Analyze, profile, and optimize deep learning workloads on state-of-the-art hardware and software platforms.

• Collaborate with researchers and engineers across NVIDIA, providing guidance on improving the design, usability and performance of workloads.

• Lead best-practices for building, testing, and releasing DL software

What we need to see:

• 5+ years of experience in DL model implementation and SW Development

• BSc, MS or PhD degree in Computer Science, Computer Architecture, Mathematics, Physics or related technical field or equivalent experience

• Excellent Python programming skills, extensive knowledge of at least one DL Framework

• Strong problem solving and analytical skills

• Algorithms and DL fundamentals

Ways to stand out from the crowd:

• Experience in performance measurements and profiling

• Experience with running large-scale workloads in HPC clusters

• Knowledge and love for DevOps/MLOps practices for Deep Learning-based product’s development.

• Solid understanding of Linux environments and containerization technologies such as Docker

• GPU programming experience (CUDA or OpenCL) is a plus but not required.