Performance Infrastructure Engineer- Data Center GPU

THE ROLE:

You will be part of a small, but dedicated team driving discrete GPU products' performance attainment solutions across hardware,softwareand the platform.We areseekinga highly skilled engineer to join our Infrastructure team, focused on building scalable solutions for workload automation and performance analysis supporting advanced machine learning workloads.

THE PERSON:

You havestrong backgroundsin Python development, Linux system administration, and build toolchainexpertise.

You have experience building or using internal workflow orchestration platforms to manage and execute distributed or multi-systemcomputeworkloads

You have familiaritywith server hardware, networking protocols, and mixed hardware/software environments

You canadapt quicklyto new tools and technologies and apply them effectively.

You can manage multiple tasks, priorities, and release cycles in parallel.

KEY RESPONSIBILITIES

• Assess and understand the current automation and performance analysis infrastructure,identifyingstrengths, gaps, and opportunities for improvement.

• Collaborate with internal teams to gather technical requirements and understand evolving needs.

• Develop aforwardlookingplan that balances reusing existing systems with building new infrastructure whereappropriate.

• Design, develop, andmaintainautomation and performanceanalysistoolingusing Python, Bash, Make, and related technologies.

• Build and enhance workflow automation solutionsusinginternallydeveloped toolsto orchestrate ML workloads.

• Develop new techniques andtoolingtooptimizeML workload execution, profiling, and analysis at scale.

PREFERRED EXPERIENCE:

• Strong development experience in Python and/or Bash (or equivalent scripting languages).

• Experience withGithub, Jenkins, or similar CI/CD and code review systems.

• Linux system administration experience preferred.

• Experience developing automated test infrastructure and orchestratingmultisystem workflowsis preferred. Ansible experience is abonus.

• Strong analytical, problemsolving, and debugging skills

• Excellent communicationskills;must be a critical thinker and self-starter.

• Ability to quickly learn and apply new tools, technologies, and frameworks.

• Networking experience preferred, including common protocols and basic debugging.

• Experience with Docker/containers and/or virtualization technologies preferred

ACADEMIC CREDENTIALS:

• Bachelor's degree in aComputer Engineering/Computer Sciencefield with7+ years of hands-on experience, or aMaster'sdegree with5+ years of relevant experience.