Own real signals
Coolant, CDUs, racks, filters, flow, pressure, and GPU context.
Careers
Build the product behind liquid-cooled AI reliability.
No open positions at the moment.
Thank you for your interest in Reliability Engine. Follow us for future job openings and company updates.
Our engineers, chemistry specialists, and data scientists work across coolant health, loop telemetry, software, and field reliability for liquid-cooled AI systems.
PhD-level chemistry expertise and hands-on systems experience connect lab evidence to practical decisions for operators.
- Coolant chemistry
- Telemetry
- ML systems
- Field reliability
Build the product
Baselines, diagnostics, APIs, alerts, and operator workflows.
Prove it works
Signals that help teams act before cooling drift hurts output.
Explore the work areas
People at work
Work happens where coolant, telemetry, and compute meet.
Each role owns a different signal, but the product only works when the signals explain one another.
Accepted baselineShared change
Flow
Temperature
Pressure
Compare signals in contextTelemetry to risk models
Coolant, CDU, rack, and GPU signals.
- Evidence
- Flow, temperature and pressure
- Work
- Compare baselines and peer racks at similar load
- Outcome
- A pattern worth investigating
Coolant sampleCDU telemetryGPU stateOperator action
Why it matters
The loop is now part of the compute stack.
Dense GPU systems depend on coolant health, flow, pressure, service history, and thermal margin. We are building the layer that reads those signals together.
Early signal
Catch drift before it becomes an incident
Read coolant, hydraulic, thermal, service, and workload signals as one operating picture.
Real systems
Turn science into product evidence
Make chemistry and field observations usable by models, alerts, reports, and customer decisions.
Useful action
Build for trust, not noise
Every recommendation should show evidence, confidence, and the next practical check.
What we build
Build the reliability layer operators can trust.
Every role owns a real part of the product: what we measure, how we model it, how we explain it, and how a data center team acts on it.
Fluid
Make chemistry visible
Turn pH, conductivity, particles, turbidity, inhibitors, and contamination patterns into live operating signals.
Systems
Connect the loop
Bring CDU, rack, pressure, flow, filter, service, and GPU context into reliability views and APIs.
Models
Find quiet drift
Create baselines, anomaly logic, confidence windows, and explanations that survive noisy infrastructure data.
Workflow
Help operators move
Support inspection, sampling, rebalancing, conditioning, cleaning, output protection, and recovery checks.
Work surface
No single discipline owns the whole problem.
The useful answers come from chemistry, telemetry, thermal behavior, service history, and production software sitting in the same room.
Chemistry and materials
Fluid health, inhibitors, particles, contamination, corrosion risk, and materials compatibility.
Signal systems
Baselines, anomaly detection, feature engineering, telemetry quality, and reliability metrics.
Thermal infrastructure
CDUs, manifolds, filters, cold plates, pressure, flow, and cooling behavior.
Operator decisions
Clear next actions that help teams inspect, sample, rebalance, clean, or protect output earlier.
Careers
Working at Reliability Engine
Are there any open positions?
There are no open positions at the moment. Follow Reliability Engine on LinkedIn, X, and YouTube for future job openings and company updates.
What experience is most relevant?
Relevant work includes time-series data, anomaly detection, fluid testing, thermal systems, field reliability, controls, high-density infrastructure, and products used by operators.