Quant Systems: Systems Developer (New York) (2D66E00)

Referment; New York (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

Referment is seeking a skilled engineer to design and improve workload scheduling and fleet-management capabilities for large-scale compute environments. You will also contribute to clustered file-system operations and tune kernel and network performance for latency-sensitive applications.

The role requires strong programming in Python, Go, or Rust, and a computer science degree or equivalent practical experience. Expertise in Linux, networking, HPC, and storage will be highly valued.

Qualifications

• Strong programming ability in Python, Go, Rust or comparable language.
• A computer science or related technical degree, or up to five years of comparable practical experience.
• Experience with Linux, networking and HPC environments is a plus.

Responsibilities

• Design and improve workload scheduling and fleet-management capabilities for large-scale compute environments.
• Contribute to clustered file-system operations and tune kernel and network performance for latency-sensitive applications.

Skills

Python

Go

Rust

Linux

Networking

HPC

Storage

GPU infrastructure

Data-centre hardware

Workload scheduling

Fleet management

Clustered file systems

Kernel tuning

Software engineering

Distributed computing

Education

Bachelor's degree in CS or related field

Job description

You will design and improve workload scheduling and fleet-management capabilities for large-scale compute environments. Additionally, you will contribute to clustered file-system operations and tune kernel and network performance for latency-sensitive applications.

Requirements: Candidates must have strong programming ability in Python, Go, Rust, or a comparable language. A computer science or related technical degree, or up to five years of comparable practical experience, is required.

Key Skills: Python, Go, Rust, Linux, Networking, HPC, Storage, GPU infrastructure, Data-centre hardware, Workload scheduling, Fleet management, Clustered file systems, Kernel tuning, Software engineering, Distributed computing