Hire Pre-Vetted Machine Learning Engineers in Under 14 Days
A model that scores well in a notebook is worth nothing until it runs reliably in production. Our ML engineers build the pipelines, serving, and monitoring that get models live and keep them there — at $35/hour, with full US-hours overlap.
✔ Sub-14-day placement ✔ US time-zone overlap ✔ Vetted on real work ✔ Cancel anytime, 2 weeks’ notice
Why senior ML engineers are so hard and expensive to hire
There’s no shortage of people who can train a model. What’s scarce — and what teams actually need — is engineers who can take a model from research to a reliable, monitored production service. That intersection of ML and solid software engineering is one of the hardest hires in tech.
In the US, that talent commands $190K–$260K and a multi-month search. Divogue places ML engineers who have already shipped models into production, vetted on real work, in under two weeks and at roughly a third of the loaded cost.
What our machine learning engineers are vetted on
Every ML engineer we place is screened on production machine learning, not Kaggle leaderboards.
| Area | What we test for |
|---|---|
| Modeling | Supervised and unsupervised learning, feature engineering, model selection |
| Frameworks | PyTorch, TensorFlow, scikit-learn, XGBoost |
| MLOps | Pipelines, model serving, versioning, monitoring, retraining |
| Data and cloud | Spark, SQL, AWS SageMaker, Azure ML, Docker, CI/CD |
| AI tooling | Daily use of Cursor, Claude, Copilot to ship faster |
What teams hire our machine learning engineers for
Production model serving
Taking models from notebook to a monitored, scalable service that holds up under real traffic.
Recommendation and ranking systems
Building and tuning the personalization engines that drive engagement and revenue.
Forecasting and prediction
Demand, churn, fraud, and risk models built on clean, reproducible pipelines.
ML pipeline automation
End-to-end training, evaluation, and retraining workflows so models stay accurate over time.
The same engineer, half the cost, a fraction of the wait
| In-House US Hire | Divogue ML Engineer | |
|---|---|---|
| All-in annual cost | $185K–$250K | ~$67K ($35/hr) |
| Time to start | 2–4 months | Under 14 days |
| Recruiting fee | 20–25% of base | $0 |
| Time-zone overlap | Full | Full US hours |
Calculate what you’d save
Drag the sliders to compare a Divogue machine learning engineer against an in-house US hire.
Estimates based on a $35/hr Divogue rate at 160 hrs/month vs a $150K–$200K all-in US salary plus a ~22% recruiting fee. Actual figures vary by role and seniority.
Trusted by teams shipping in production
Divogue engineers are embedded with companies building real products today — including Riskcast, BGO Software, and everyone.ai — across construction technology, healthcare software, and ML infrastructure.
Frequently asked questions
What’s the difference between an ML engineer and a data scientist?
A data scientist explores and models; an ML engineer ships and operates those models in production reliably. We place the latter, with strong software engineering skills.
How quickly can one start?
Most placements are live in under 14 days from your first call.
Which frameworks do they know?
PyTorch, TensorFlow, scikit-learn, and XGBoost, plus MLOps tooling for serving and monitoring on AWS and Azure.
Do they overlap US working hours?
Yes. We source from LATAM and APAC specifically for engineers who work your business hours.
What if the engineer isn’t the right fit?
Cancel with two weeks’ notice, no penalty. We’ll help you find a better match if you’d like to continue.
Book a free intro call
Pick a time that works for you. We’ll walk through your stack, your needs, and show you matched engineers within days.
Ready to add a machine learning engineer to your team?
Book a free 30-minute call. Tell us what you’re building and we’ll show you matched ML engineers within days.