Nvidia Senior MLOps Engineer GenAI Framework 

United States, California 

39094096

What you’ll be doing:

• Architect and manage the continuous integration pipelines and release processes of our Generative AI framework and libraries related to and .

• Design and implement efficient and scalable DevOps solutions to allow our fast growing team to release software more frequently while maintaining high-quality and maximum performance.

• Work with industry standard tools (Kubernetes, Docker, Slurm, Ansible, GitLab, GitHub Actions, Jenkins, Artifactory, Jira) in hybrid on-premise and cloud environments.

• Assist with cluster operations and system administration (managing: servers, team accounts, clusters).

• Accelerate research and development cycles by automating recurring tasks such as accuracy and performance regression detection.

• Developing new quality control measures, e.g. code analysis, backwards compatibility, and regression testing, while employing and advancing best-practices.

• Work closely with DL frameworks and libraries (CUDA, cuDNN, cuBLAS, and PyTorch) teams and with other engineering teams within NVIDIA that provide software, testing, and release related infrastructure.

What we need to see:

• BS or MS degree in Computer Science, Computer Architecture or related technical field (or equivalent experience) and 6+ years of industry experience in DevOps and infrastructure engineering.

• Strong system level programming in languages like Python and shell scripting.

• Extensive understanding of build/release systems, CI/CD and experience with solutions like Gitlab, Github, Jenkins etc.

• Experience with Linux system administration.

• Proficient with containerization and cluster management technologies like Docker and Kubernetes.

• Experience in build tools, including Make, Cmake.

• A strong background in source code management (SCM) solutions such as GitLab, GitHub, Perforce, etc.

• Well-versed problem-solving and debugging skills.

• Great teammate who can collaborate and influence others in a dynamic environment.

• Excellent interpersonal and written communication skills.

Ways to stand out from the crowd:

• Proven-track record with GPU accelerated systems at scale.

• Well-versed in DL frameworks such as PyTorch, Jax, or TensorFlow.

• Expertise in cluster and cloud compute technologies, e.g.: SLURM, Lustre, k8s

• Software and hardware Benchmarking on high-performance computing systems.