Systems Engineer, Server, Cloud & Data Infrastructure — Large-Scale Computing for Drug Discovery and Machine Learning Research
Durham, NC · Hybrid (three days per week on site, two days optional remote)
The Opportunity
Research computing at this scale means keeping storage measured in tens of petabytes, GPU clusters numbering hundreds of GPUs, and Linux clusters with CPU core counts from the low thousands into the tens of thousands reliable, fast, and secure, while making all of it easy for drug discovery and machine learning researchers, and the AI agents working alongside them, to use. The hire will build and run the infrastructure, software, and networks behind that work, and their decisions will determine how dependably researchers can put that capacity to use.
The client is a New York–based research company in computational biochemistry and drug discovery, with openings at its New York City offices and at its data centers in Pittsburgh, PA and Durham, NC. This posting is for the Durham, NC site. StaffRight Associates is recruiting for this opportunity and the company is filling more than one position.
The Research Environment
The client pairs specialized computing hardware it designed in-house with high-performance commodity systems, machine learning, and other computational methods to model molecular behavior at the atomic level and design highly selective drug candidates. That work spans in-house programs and drug discovery efforts run with outside partners, and the infrastructure serves both human researchers and AI agents.
What You'll Do
- Architect and operate the client's server, cloud, and data infrastructure, from public cloud deployments to applications running on Kubernetes.
- Manage storage systems holding research data on the order of tens of petabytes.
- Scale and maintain GPU clusters numbering hundreds of GPUs and Linux clusters whose CPU core counts reach into the tens of thousands.
- Sustain specialized, massively parallel custom supercomputing hardware alongside the general-purpose clusters.
- Engineer high-performance RDMA fabrics and the wide-area networks that link sites together.
- Administer identity management infrastructure across the environment.
- Deliver these resources to researchers and AI agents in a way that is both secure and easy to work with.
- Communicate clearly with research users and engineering colleagues about how the systems work and how to use them well.
What You Bring
- Strong grounding in Linux fundamentals, including File Systems, Networking, and Processes, plus hands-on Linux Systems Administration experience.
- Expertise in any of the following is relevant: Large-Installation Systems Administration, HPC Systems Engineering, Python programming and scripting for Distributed Systems, High-Performance Data-Parallel Computing, Systems Software, Software Development, User Support, Kubernetes platform engineering, and Email & Data Retention practices.
- The client weighs versatility, intellectual curiosity, and a record of strong accomplishment more heavily than depth in any single area above.
- Ability to explain technical reasoning to experts and non-specialists alike.
- Candidates at all experience levels are welcome to apply.
Paths Into This Role
Candidates whose graduate or doctoral work centered on high-performance or distributed computing, operating systems, computer networking, or large-scale scientific computing in fields such as Computational Physics or Computational Chemistry will find that training maps closely onto this work.
Compensation & Total Rewards
- Expected annual base salary: $250,000–$800,000, set according to factors such as experience, education, and location.
- Sign-on and year-end bonuses as variable pay.
- A generous benefits package with relocation support.
Joining StaffRight Associates
When you work with StaffRight Associates, you get a recruiting partner who understands the substance of your work and represents it accurately to the teams hiring for it. We focus on matching researchers with problems worth their training, and we'll give you a clear, candid picture of each opportunity before you decide to pursue it.
