Cloud Computing Engineer (HPC)

Location: New York, NY; hybrid work with at least 3 days per week in the New York City office

Salary: $195,000–$225,000 per year, excluding potential bonuses

Company

hirify.global is a quantitative investment manager with a 30+ year track record focused on building high-quality research and trading products.

What you will do

• Design and implement cloud-based high-performance computing systems for research and model implementation.
• Operate and support a 24/7 production HPC platform, maintaining reliability through automation and quality engineering.
• Build automation across CI/CD pipelines, production metrics, monitoring, software distribution, and platform operations.
• Partner with researchers and engineers to deliver efficient, reliable cloud HPC systems and lead platform evolution projects.
• Manage capacity and optimize benchmarks for large-scale workloads supporting research and trading.

Requirements

• Bachelor’s or master’s degree in engineering or applied sciences, or equivalent professional experience.
• 5+ years of experience building and shipping software systems.
• 2+ years of experience running production compute platforms and scheduling systems.
• Strong software engineering skills in system design, testing, debugging, and production reliability, with fluency in Python, Go, Rust, or a similar language.
• Experience with infrastructure as code, such as Terraform, plus excellent written and verbal communication skills.
• Hands-on experience managing inference and training hardware at scale; knowledge of NVIDIA GPU management, Kubernetes, Slurm, and large-scale compute systems.

Nice to have

• Experience supporting researchers or other developers.

Culture & Benefits

• Work in a small, flat team spanning research, software implementation, and platform infrastructure.
• Collaborate directly with research and model implementation teams.
• Join a close-knit, diverse community with a strong employee-retention record.
• Work on a 24/7 research environment supported by automation and operational quality practices.