N

Nscale

Develops and operates AI data centers

Design Reliability Engineer - Power and Energy

Mechanical Maintenance and Reliability EngineeringConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 5 years of reliability engineering experience on power generation, process, or mission-critical facilities, including significant ownership of RAM analysis for large, redundant systems.
  • Demonstrated RAM modeling capability using discrete-event simulation such as BlockSim or Raptor and closed-form analytical methods, with sound judgment about which method to use.
  • Proven FMEA/FMECA leadership on major rotating, electrical, or process equipment, and familiarity with standard failure data sources such as IEEE 493, OREDA, and the IEEE 3006 series and their limitations.
  • Experience allocating availability targets from commercial commitments down to systems and defending the analysis to executives, customers, or insurers.
  • Ability to identify common-mode and dependent failure mechanisms and risks that redundancy calculations may conceal.
  • Working software capability, including scripting analyses with Python or similar and structuring reliability data for automation.
  • Experience integrating reliability analysis into live design processes on major capital projects and influencing engineering decisions.
  • Ability to run a scope with minimal oversight by establishing the basis, setting the pace, and escalating with solutions.
  • Bachelor's degree in Mechanical, Electrical, Chemical, or Reliability Engineering or a related field.
  • Willingness and ability to travel regularly to project sites and vendor facilities, typically 15–25%, with higher travel during commissioning campaigns.

Responsibilities

  • Own the availability model of record for each generation campus, including living system-level RAM models spanning generation, electrical distribution, fuel supply, and cooling.
  • Allocate committed service-level agreement targets through the system, including subsystem availability budgets, redundancy requirements, mean time to repair assumptions, and sparing and maintenance strategies.
  • Use Monte Carlo simulation such as BlockSim or equivalent where appropriate and closed-form analytical models where faster or more auditable, and defend the method selected.
  • Identify common-mode and dependent failures, including shared fuel supply, shared cooling, control system dependencies, and site-wide events that redundancy counts may conceal.
  • Report availability using both single-path and contracted-capacity views, including sensitivities showing which assumptions drive the result.
  • Own the FMEA/FMECA program for major equipment and systems, including engines and generators, battery energy storage systems, switchgear and transformers, fuel gas systems, and cooling infrastructure; establish baselines and update them as designs mature.
  • Curate the failure-rate and repair data used in models from industry sources such as IEEE 493 and OREDA, challenge original equipment manufacturer data, and incorporate field data as it accumulates.
  • Use reliability analysis to influence redundancy configuration, single-point-of-failure treatment, equipment selection, testability, and maintainability requirements while designs can still change.
  • Integrate reliability analysis into design reviews, HAZOPs, and vendor evaluations as a routine discipline.
  • Own the reliability core of Nscale's digital twin, including model architecture, data structures, and analytical methods for recomputing availability as system state, configuration, and failure data change.
  • Define operational data requirements for event capture, failure coding, downtime attribution, and run-hour tracking so the digital twin receives reliable data from the start of operations.
  • Work with software and data teams to move RAM analysis from static studies into instrumented, queryable tooling and promote analytical approaches that are automatable and auditable.
  • Establish a feedback loop comparing measured availability with model predictions, recalibrating models, and tracking reliability growth from commissioning onward.
  • Support commissioning and startup with reliability input, including burn-in and reliability-run design, startup failure tracking, and acceptance criteria grounded in the availability model.
  • Lead and support root-cause analyses of significant failures and availability events, and drive corrective actions into designs, models, and standards.
  • Develop reliability engineering standards, methods, and reference models for reuse across campuses.
  • Direct RAM and reliability consultants by setting the basis of work, reviewing and challenging methodologies and deliverables, and integrating results across power and mechanical scopes.
  • Coordinate with data center reliability and operations teams so availability is engineered and measured across the full path to the GPU.
  • Build internal reliability engineering capability over time, including developing a small team as the portfolio grows.
  • Communicate complex reliability analysis to project leadership and executives, including confidence, sensitivity, and risk.

Desired Qualifications

  • Exposure to digital twin, telemetry-driven reliability, or operational availability measurement programs.
  • CRE certification or equivalent.
  • PE license.

About the Company

Nscale

Develops and operates AI data centers

Nscale provides end-to-end AI infrastructure by designing, owning, and operating data centers, GPU compute, networking, storage, and managed AI cloud services for enterprises, governments, and AI developers. It offers a full stack from colocated data centers to high-performance GPUs and networking, plus managed cloud services, so customers can run AI workloads efficiently with sovereign European deployments powered by renewables and US campus expansions. Unlike many peers, Nscale combines complete infrastructure ownership with a focus on sovereign, renewable-powered sites and strong capital partnerships to support large-scale, secure AI workloads. Its goal is to become a leading, scalable AI infrastructure provider that enables governments, enterprises, and AI builders to deploy and run AI applications quickly and securely on sovereign data centers with global capacity.

Company size
1,001-5,000
Company stage
Debt Financing
Total funding
$9.8B
Headquarters
London, United Kingdom
Founded
2024

Recently Posted Jobs

Explore more opportunities at Nscale

See more jobs