Cloud Computing Engineer

Why join us?

Responsibilities

• We are a small flat team sitting at the cross-section of research, implementation, and platform infrastructure. Our team responsibilities span many areas. Including:
• Design and implementation of cloud-based HPC systems. Our projects involve equal parts engineering and operations for success in our fast-moving environment. You will be expected to conceive and implement projects small and large in the intersection of HPC scheduling, metrics, containerization, software distribution, accelerated compute performance/efficiency, cloud architecture.
• Running our HPC plant day-to-day. Our research environment is up 24/7, and we want to keep it that way. Everybody on the team contributes to the support of our platform, which thankfully is light because of our automation and quality work.
• Implementing automation. We will always choose to work smart over working hard. You will be responsible for conception and implementation of automation from CI/CD pipelines to production metrics and monitoring of our cloud HPC platform.
• Obsessive User Focus. All members of the team are expected to partner with researchers and engineers to deliver high-quality cloud HPC systems that are efficient and reliable. This includes leading projects to evolve it as our needs change.
• Capacity management and benchmark optimization. Our demand for scale and performance is constant and involves challenging optimization problems for workloads critical to research and trading
• Below is a list of skills and experiences we think are relevant. Even if you don’t think you’re a perfect match, we still encourage you to apply because we are committed to developing our people.
• Bachelors or Masters degree in an Engineering or Applied Sciences field from a rigorous academic program or equivalent professional experience.
• 5+ years of experience building and shipping software systems
• 2+ years running production compute platforms and scheduling systems.
• Mastery of core software engineering concepts such as system design, testing, debugging, building reliable production systems with fluency in a language such as Python, Go, Rust etc.
• Experience with a cloud-based infrastructure-as-code tool such as Terraform
• Excellent written and verbal communication skills
• Experience working with or supporting researchers and/or other developers is a plus
• Hands-on experience managing inferencing and training hardware at scale
• Knowledge of NVIDIA GPU management, Kubernetes, Slurm, and the wider large-scale compute ecosystem
• The salary range for this role is between $195,000 and $225,000. This range is not inclusive of any potential bonus amounts . Factors that may impact the agreed upon salary within the range for a particular candidate include years of experience, level of education obtained, skill set, and other external factors.
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