For models that hold up on data they haven't seen yet
Hire ML Engineers
A model that performs beautifully on a held-out test set can still fail quietly once real, messier production data starts flowing through it. These engineers are tested on catching that exact gap. They are then placed to keep your models honest well past the day they were trained.
- Profiles typically within two business days
- Replacement Assurance: If an engineer is not the right fit, a replacement from our own team starts within ten business days of your request, at no extra cost
- You interview before placement if you want to
- At least four hours a day inside your core working hours. Two hours plus a daily written handover for the US West Coast
- 14-Day Risk-Free Trial: For your first engineer on a time-and-materials engagement. Up to 14 calendar days or 80 logged working hours, whichever comes first. End within the trial and pay nothing for trial hours. Continue beyond it and those hours are billed at the agreed rate. Unpaid trial work remains ours if you stop
How we evaluate an ML engineer
The tasks, the signals and the onboarding, walked through by the people who run them.
90 secSample frame · video pending01Published, not promised
The assessment behind every engineer we send.
The ML bar is published, not assumed: five competencies, the exact task behind each, and the shortcut that ends a candidacy. Most interviews ask which algorithm someone prefers. This one asks whether the model can be trusted on new data.
What we listen forWhether they catch leakage before it looks like success.
A fraud-detection model performs suspiciously well in testing and much worse in production. Diagnose why.
- What strong sounds like
- Investigates whether a feature encodes information that would not be available at real prediction time, the classic leakage signature.
- What gives us pause
- Attributes the gap to production data being 'noisier' without investigating specific features for leakage.
Python · scikit-learn · PyTorch/TensorFlow
Airflow · Feature Stores · MLflow
Cross-validation · A/B Testing · Drift Detection
Docker · Kubernetes · Weights & Biases
02Who you actually meet
The bench, priced by seniority.
Three seniority tiers, priced honestly, and the engineers who fill each one today. Real names and photos take their place as soon as we can share them, and the standard stays exactly the same.
Engineer
Features scoped tightly enough to ship inside your review bar, every time.
The right first hire when your seniors already set the standard.
$2,800–3,600per month
Senior
The hard module nobody else wants to own, taken end to end.
Ownership of a surface, without adding a manager to your headcount.
$3,900–4,900per month
Lead
The workstream's decisions, made daily, so nothing waits on a meeting.
One accountable voice for a pod or initiative that can't afford to drift.
$5,200–6,500per month
ML engineer
3 yrs · US East overlap
Traced a fraud model's suspicious accuracy to a leaked feature before it reached production
- Python
- scikit-learn
- MLflow
Senior ML engineer
7 yrs · UK + EU hours
Caught that a celebrated accuracy score meant little given real class imbalance, and fixed the metric
- PyTorch
- Feature Stores
- Cross-validation
Lead ML engineer
10 yrs · EU + US split
Owns the drift-monitoring standard catching degradation months before customers would notice
- Architecture
- Airflow
- Kubernetes
- Drift Detection
Indicative ranges, representative profiles. Exact figures and named engineers arrive with your shortlist.
Weighing this against a freelancer, a direct hire or a marketplace? See the honest comparison
03From stated problem to first change
Four steps, each closing on evidence.
This process tests judgement about real data, not familiarity with algorithm names. You can walk away at any step owing nothing.
Define the modelling problem
We map where your models' evaluation or reproducibility is actually shaky. The brief names that gap, not a list of algorithms to know.
A brief your leads signMatch against your pipelines
Candidates are screened against your actual data volume, your evaluation practices and your review culture. Three engineers who understand your pipelines beat thirty who list ML frameworks.
Three fits, not thirty CVsAssess on the real tasks
Your team can watch the leakage-diagnosis and metric-selection exercises directly. What shows up is statistical judgement, not familiarity with a specific library's syntax.
Evaluation you can watchOnboard inside your process
Access, standards and a first bounded improvement to a real model. Continuity cover keeps a departure from ever becoming a silent gap in monitoring.
A merged change in week one
04Evidence and proof
Engineers who kept the model honest on real data.
ML proof is a model that held up on data it hadn't seen, not a leaderboard score. Every record here waits for written client approval before production.
Explore case studies
A fraud model's suspicious accuracy traced to a leaked feature.
Vela Ledger's fraud model scored implausibly well in testing and noticeably worse once live. An embedded ML engineer traced it to a feature that encoded information unavailable at real prediction time and rebuilt the feature set correctly.
- Leaked features found
- One, before deployment
- Time to corrected model
- One sprint
He caught that our celebrated accuracy number meant almost nothing given how imbalanced our data actually was.
Sample · pending client approval
The drift monitoring she set up caught a real degradation two months before a customer would have noticed.
Sample · pending client approval
05Three shapes, one standard
Add an engineer, a modelling pod, or a practice.
Most teams start with one engineer strengthening an existing model. Some need a pod that owns modelling and monitoring end to end. Where a whole modelling platform delivered matters more than one hire, we point you to the practice instead.
- Team extension
One named engineer
A single ML specialist inside your direction and your reviews. The smallest commitment, and how most engagements start.
- Delivery pods
A modelling pod
A pod with its own lead that owns feature design, evaluation and drift monitoring end to end. You set priorities while the pod carries delivery.
- Data & Cloud
The practice, not the person
Where a whole modelling platform delivered matters more than one hire, data and cloud engineering takes that scope on instead.
06Straight answers
Asked before an ML hire is signed.
01What does an ML engineer actually do day to day?
Designs features and checks for data leakage, chooses evaluation metrics that reflect the real cost of an error, and builds monitoring that catches drift before a customer does. The unglamorous majority of the work is verifying a result rather than producing a new one.
02Do you hire for a specific ML framework?
Matched to your actual stack. Evaluation and drift-detection judgement transfers across scikit-learn, PyTorch and TensorFlow, then the evaluation confirms familiarity with whichever you run.
03Can we run our own interviews as well?
Yes, the published tasks are built to be reused directly. Several clients now run the leakage-diagnosis exercise themselves in later hiring rounds.
04How fast can an ML engineer start?
A shortlist arrives within days from a bench evaluated continuously. Real start speed after that tracks how quickly your side can grant data and pipeline access.
05Who manages the engineer day to day?
Your leads set priorities and hold the evaluation standards. We take on the employer side instead: continuity, payroll, replacement risk and the handoff work when plans change.
06What if the engineer is not the right fit?
Say so within the first weeks and the swap is funded by us. The next engineer arrives briefed on the exact gap, which keeps a repeat mismatch rare.
07Do your ML engineers use AI coding tools themselves?
Yes, to scaffold pipeline code and test harnesses. The models they build for you are governed separately, through the evaluation and drift monitoring described above.
08How does pricing work for a hired ML engineer?
Pricing tracks the bands above for this specialism, with the shortlist fixing the number once scope is set. Monthly billing, no placement fee, and notice-period terms at exit.