Site Reliability Engineer
Nscale is hiring a Site Reliability Engineer in Houston, New York and San Francisco, United States. It pays $130k-$200k a year and Level rates it ; you can apply on Nscale.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- The posting mentions Python, Go and Kubernetes among its tools and skills.
- Level classes it as a mid-level role.
- Nscale has 299 other open roles on Level.
AI in this role
Site Reliability Engineer to build and maintain high-performance GPU cloud infrastructure and automation for AI workloads.
About Nscale
Nscale is the GPU cloud built for AI. We run high-performance, cost-efficient infrastructure for AI-native
startups and global enterprises, from bare metal up through the platform services teams actually build
on. Our culture runs on ownership, accountability, and speed. We move with urgency, we tell each
other the truth, and everyone here stays close to the infrastructure that makes AI work.
The Role
This is a career-level SRE role for someone who wants to own systems, not just watch them. You'll take
real surface area: the automation and tooling other engineers depend on, and the reliability of
production services running AI and GPU workloads at scale. You'll sit in the incident rotation, and you'll
be expected to make the systems you touch quieter over time.
What You'll Do
• Build and own the automation and tooling that keeps the platform running; treat operational toil as
a bug to be fixed, not a fact of life.
• Define and maintain SLOs, SLIs, and the dashboards that make service health obvious at a glance.
• Take point during incidents; troubleshoot under pressure, drive root cause analysis, and run post-
incident reviews that actually change the system.
• Investigate performance and reliability problems across Linux, networking, and distributed services,
then fix them at the source.
• Partner with Engineering, Networking, and Infrastructure teams to raise the reliability bar across the
stack.
• Improve availability, scalability, and efficiency through code, not manual effort.
What You'll Bring
• 3-6 years in SRE, systems engineering, or software engineering, including time running production in
a data center or cloud environment.
• Strong programming skills (Python, Go, or similar) and a genuine bias toward automating the work
away.
• Solid command of Linux, networking fundamentals, and distributed systems.
• A track record of troubleshooting live production issues and owning the fix through to the retro.
• Fluency with monitoring and observability; metrics, logs, dashboards, and alerting.
• Comfort in a fast-moving environment where priorities shift and you fill gaps without waiting to be
asked.
Nice to Have
• Experience with AI or GPU workloads, or high-performance computing (HPC).
• Familiarity with high-performance networking (InfiniBand, RDMA).
• Kubernetes, plus virtualized or bare-metal environments.
On-Call and Pace
A quick note on the shape of the job. This role sits close to production, so there is an on-call rotation,
and some weeks are busier than others. We share it fairly, and we treat every page as a signal worth
acting on rather than just an interruption. The goal is to make the systems quieter over time, so each
rotation asks less of the person carrying it. If you take ownership of what you run and like leaving it in
better shape than you found it, you'll do well here.
What We Offer
• Competitive base plus equity, reviewed every 12 months.
• Real scope early, and a progression plan built around the skills you want to sharpen.
• Flexibility that treats you as an adult; we care that the work gets done, and we trust you to shape
your day.
Salary Range
$130,000 - $200,000 USD. Actual compensation varies with skill set, experience, and location, and the
role may be eligible for bonus and equity.
Equal Opportunities Statement
At Nscale, we are committed to fostering an inclusive, diverse, and equitable workplace. We believe that a variety of perspectives enrich our work environment, and we encourage applications from candidates of all backgrounds, experiences, and abilities. We strongly encourage applications from people of colour, the LGBTQ+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio-economic backgrounds.
If there’s anything we can do to accommodate your specific situation, please let us know.
For information on how Nscale handles candidate personal data, please see our Employee & Candidate Privacy Notice: Here.
Nscale does not accept unsolicited candidate submissions from recruitment agencies.
How we rate this
Site Reliability Engineer at Nscale rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where sre was part of your work. What did you do?
- Tell me about a project where systems engineering was part of your work. What did you do?
- Tell me about a project where devops was part of your work. What did you do?
- Tell me about a project where infrastructure was part of your work. What did you do?
- Tell me about a project where gpu workloads was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Sre, Systems Engineering, DevOps, Infrastructure, and GPU Workloads. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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