HPC Infrastructure Engineer

Stack

Responsibilities

• ▹Design, build and maintain high-performance computing (HPC) infrastructure
• ▹Develop and implement scalable distributed systems for complex computing tasks
• ▹Scale services and systems from smaller deployments of ~50 nodes into larger ~250+ node clusters
• ▹Use orchestration, configuration management, virtualization, Linux, CLI, deploy tools, monitoring and APM
• ▹Monitor HPC system performance and implement improvements to ensure scalability and efficiency
• ▹Optimize engineering and deliverables to balance feature development, product quality and service reliability

Requirements

• ▹At least 3-5 years of experience in HPC infrastructure, systems engineering or a similar role
• ▹Strong understanding of systems engineering principles and scaling strategies
• ▹Deep knowledge of at least one of AWS, GCP or Azure, preferably AWS
• ▹Strong Linux skills
• ▹Experience with data architecture and large-scale data processing
• ▹Ability to deliver with top-level engineering goals and milestones, figuring out the path yourself
• ▹3 references are required

Nice to have

• ▹HPC CFD experience
• ▹Scaling services and systems from single-region clusters into multi-regional deployments
• ▹Experience with clusters of greater than 100 nodes
• ▹Building on-prem systems (data center, rack-n-stack, etc.)
• ▹Distributing workloads onto both cloud and on-prem systems
• ▹Balancing business needs, product delivery dates and customer satisfaction
• ▹Backend work like SQL, API development and serverless
• ▹Software engineering in Python and/or other programming languages
• ▹Using Linux as your primary desktop workstation environment

Soft skills

What we offer

• ▹Remote work
• ▹Working with some of the most experienced people across HPC, ML, simulation and quantum
• ▹Opportunity to work on other projects (ML, simulation, quantum)

About the company

Insight Softmax is an organization in machine learning, data science and high-performance computing (HPC), providing computing solutions for research, science, manufacturing, finance and data analytics.