FluidStack
Mechanical Engineer, Demand Management
About this role
Fluidstack seeks a Mechanical Engineer to design and optimize cooling systems for AI data center infrastructure that can flexibly respond to grid demands while managing thermal risk. You'll model thermal dynamics, develop mechanical strategies for demand flexibility, and validate approaches through field testing to turn cooling into a controllable grid asset.
What you'll do
- Engineer cooling systems capable of safely handling load flexibility and power demand swings
- Model thermal inertia and quantify per-site flexibility capacity in terms of magnitude and duration
- Design mechanical strategies including setpoint adjustments, staging sequences, and thermal storage solutions
- Conduct instrumented field validation campaigns to prove models before fleet deployment
- Collaborate with controls engineers to integrate mechanical and control strategies as unified systems
- Quantify operational risk using thermal margins, temperature limits, and time windows
What they're looking for
- HVAC and mechanical systems design for data centers or industrial plants
- Dynamic thermal modeling (transient analysis, not steady-state only)
- Field instrumentation and measurement validation
- Thermal risk quantification and margin analysis
- Controls integration and system-level thinking
- Liquid cooling systems (preferred)
- Dynamic simulation tools: Modelica or TRNSYS (preferred)
- Psychrometrics and thermal storage fundamentals (preferred)
Opens the official application on the employer’s site. No login required.
FluidStack
FluidStack builds AI infrastructure at scale, developing data centers and warehouse operations designed to handle gigawatt-capacity compute deployment. The company is hiring for warehouse engineers, data center operations specialists, product engineers, and people leaders to support rapid infrastructure expansion across multiple sites.
- Website
- fluidstack.io
Likely interview questions
- Walk us through a past project where you modeled a thermal system dynamically and then validated it against real measurements—what gaps did you find and how did you address them?
- How would you approach quantifying how long a data center cooling system could sustain a 50% load reduction without exceeding safe temperature limits?