Find the first useful signal
Follow the physical story across coolant, hydraulics, service history, thermal response, and workload.
Careers
Build reliability for liquid-cooled AI.
Join a small team working on a hard physical problem: keeping liquid-cooled AI systems dependable as rack density climbs.
You will work close to the coolant, the data, and the customer. Good ideas move quickly from analysis to a product an operator can test in the field.
- Work
- Coolant, CDUs, racks, telemetry, models, and field evidence.
- Standard
- Recommendations that operators can question, test, and verify.
The work
Build what operators can trust in the field.
The work starts with what changed in the coolant, how the loop responded, and whether compute margin moved.
Ship work that reaches the field
Turn chemistry and engineering evidence into models, alerts, and decisions customers can use.
Earn operator trust
Make every recommendation clear enough to question, test, and verify.
Where you can contribute
Four ways to move the product forward.
This is early-stage work with real ownership. You will shape the product, the technical standard, and how customers use both.
Turn chemistry into an operating signal
Use pH, conductivity, particles, turbidity, inhibitors, and contamination patterns to explain coolant condition.
Connect the loop
Bring CDU, rack, pressure, flow, filter, service, and GPU data into reliability views and APIs.
Find quiet drift
Create baselines, anomaly logic, confidence windows, and explanations that hold up in noisy infrastructure data.
Guide the response
Turn findings into inspection, sampling, rebalancing, conditioning, cleaning, output protection, and recovery checks.
Chemistry and materials
Fluid health, inhibitors, particles, contamination, corrosion risk, and materials compatibility.
Data 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 for inspection, sampling, rebalancing, cleaning, or output protection.
Open roles
Build the first version of something that needs to exist.
We are hiring across data science, production ML, coolant chemistry, and field reliability. Each role works directly on the product and the customer problem.
Remote / San Francisco, CA
Data Scientist
Turn noisy coolant, hydraulic, thermal, and workload data into the first signal an operator should trust.
You enjoy ambiguous data, care about the cost of a wrong alert, and want your work to change a physical operating decision.
Own
Healthy baselines
Work with
Telemetry, Python, SQL
Outcome
Fewer false positives
Role detailsClose details
Why this role matters
- Your analysis will decide which changes deserve attention, which are normal, and what the product should recommend next.
First six months
- Define healthy baseline windows for coolant condition, pressure, flow, delta T, service events, and GPU thermal response.
- Build drift views that separate workload movement from loop restriction, fluid degradation, maintenance events, and sensor noise.
- Design labels, validation plans, and feedback loops with chemistry, field reliability, and product teams.
What you bring
- Strong Python and SQL for cleaning data, building features, and making analysis reproducible.
- Experience with time-series data, anomaly detection, statistical baselines, model validation, or risk scoring.
- Comfort with missing values, calibration drift, outliers, service resets, and changing operating regimes.
- Ability to explain uncertainty plainly to software, field, chemistry, thermal, and customer-facing teams.
You'll thrive here if
- You have shipped analysis that changed an operational decision, not only a dashboard.
- You care about precision, recall, and the cost of a wrong alert.
- You enjoy physical systems and can work without perfect labels on day one.
Remote / San Francisco, CA
Machine Learning Engineer, Predictive Reliability Systems
Take reliability models from raw telemetry to production decisions, with the pipelines, monitoring, and explanations they need to hold up in the field.
You're comfortable owning the gap between a promising model and a production system customers can rely on.
Own
Production reliability models
Work with
Python or TypeScript pipelines
Outcome
Explainable alerts
Role detailsClose details
Why this role matters
- You will help make the product fast, explainable, and dependable enough for operators to use when cooling margin is at stake.
First six months
- Design pipelines that combine coolant health, CDU behavior, thermal response, maintenance context, and workload state.
- Build explainability, model monitoring, data-quality checks, and versioned evaluation for field validation.
- Turn predictions into APIs and operator workflows without creating alert fatigue.
What you bring
- Strong Python and/or TypeScript, with experience shipping ML or data systems beyond notebooks.
- Practical judgment across batch or streaming pipelines, model evaluation, model monitoring, and data versioning.
- Experience with time-series forecasting, anomaly detection, classification, ranking, or probabilistic risk scoring.
- Good instincts around precision, recall, alert thresholds, explainability, and when a model should defer instead of guessing.
You'll thrive here if
- You write production code and still care deeply about model behavior.
- You can debug a pipeline, a bad label, and a confusing operator experience in the same week.
- You have seen industrial telemetry, observability, controls, digital twins, reliability, or infrastructure data.
Remote / San Francisco, CA
Coolant Chemistry & Materials Engineer
Bring coolant chemistry and materials behavior into the daily operating picture for liquid-cooled AI systems.
You can move from fluid science to a practical sampling plan, a defensible limit, and a clear explanation for an operator.
Own
Coolant interpretation
Work with
Chemistry and materials
Outcome
Field-ready limits
Role detailsClose details
Why this role matters
- Your work will connect lab evidence, side-stream sensing, and field samples to the product decisions that protect hardware and heat-transfer performance.
First six months
- Define which coolant measurements matter, how often they matter, and what movement should change an operator decision.
- Map chemistry changes to risks such as corrosion, fouling, deposits, filter loading, biological growth, and heat-transfer loss.
- Create validation plans that help data teams build labels, thresholds, and confidence around coolant-health models.
What you bring
- Background in chemistry, materials science, chemical engineering, corrosion, coolant formulation, water treatment, or fluid reliability.
- Working knowledge of pH, conductivity, turbidity, particles, inhibitors, organic acids, microbial risk, contamination, and corrosion mechanisms.
- Ability to design test plans, sampling protocols, acceptance windows, and failure-analysis workflows.
- Comfort collaborating with software and data teams so chemistry becomes structured data, not only lab notes.
You'll thrive here if
- You can explain chemistry in a way operators and software teams can act on.
- You know where lab certainty ends and field judgment begins.
- You have worked with glycol/water loops, CDUs, cold plates, data centers, semiconductors, or industrial cooling.
Remote / San Francisco, CA
Field Reliability Engineer, Liquid Cooling Systems
Make sure Reliability Engine reflects how liquid-cooled systems are actually commissioned, serviced, diagnosed, and brought back to health.
You can move between telemetry and the physical system, then write a procedure a technician will actually use.
Own
Operator workflows
Work with
Telemetry and physical checks
Outcome
Recovery after action
Role detailsClose details
Why this role matters
- You will turn field judgment into product workflows and help customers prove that a maintenance action really restored the loop.
First six months
- Turn inspection, sampling, balancing, filter changes, cleaning, and recovery checks into product workflows.
- Review abnormal behavior around maintenance events, pump changes, filter loading, coolant conditioning, and thermal response.
- Validate whether alerts and recommendations match what field teams can safely check or do next.
What you bring
- Experience with data center infrastructure, liquid cooling, mechanical systems, controls, thermal systems, reliability, or field engineering.
- Ability to read telemetry and connect it to physical causes: restriction, imbalance, pump behavior, filter loading, air, fouling, or sensor problems.
- Clear writing for procedures, root-cause notes, customer-facing explanations, and engineering handoffs.
- Comfort working across hardware, software, operations, and customer environments where data is incomplete.
You'll thrive here if
- You have commissioned, supported, troubleshot, or operated systems where uptime mattered.
- You know the difference between an elegant recommendation and one a technician can execute.
- You have seen direct-to-chip cooling, CDU commissioning, GPU clusters, facilities operations, or incident/postmortem work.