Hire ML engineers from Eastern Europe
Senior ML engineers available on a time & material basis. Start within 2 weeks. We act as employer of record — no entity setup, no local HR.
What ML engineers actually do
ML engineers own the full model lifecycle. That means scoping the problem, designing feature pipelines, training and evaluating models, deploying them to production, and monitoring drift over time.
This is different from integrating an API call to OpenAI. ML engineers work at the model layer — they decide the architecture, own the training loop, and are accountable for what the model does in production.
Typical work includes:
- Feature engineering and feature store management
- Model training, tuning, and evaluation (offline and online metrics)
- MLOps: experiment tracking, model registry, CI/CD for models, A/B rollout
- Production serving: latency, throughput, fallback strategies
- Retraining pipelines triggered by data drift or business events
If your requirement is integrating existing AI APIs into an application, see AI developers. If you need someone who builds and owns the model itself, you need an ML engineer.
Seniority bands and T&M rate bands
All rates are illustrative EUR/month employment cost (what you pay staffai.eu). No payroll taxes, benefits admin, or overhead on your side — we handle that.
| Band | Experience | EUR/month (employment cost) |
|---|---|---|
| Mid | 3–6 years | EUR 3,500–4,500 |
| Senior | 7–12 years | EUR 5,000–7,000 |
| Staff | 12+ years | EUR 7,000–9,500 |
US equivalent loaded cost for a senior ML engineer (salary + benefits + employer taxes) typically runs USD 18,000–28,000/month. The gap is real and consistent.
Engagement terms
- Billing: time & material. You pay for hours worked, billed weekly or monthly.
- Scale: add or remove engineers week-by-week. No fixed headcount commitments.
- Employer of record: We employ the engineer. You get the work, not the HR overhead.
- Ramp time: engineers typically join your sprint within 2 weeks of contract signature.
- IP and legal: work-for-hire under EU directives. GDPR-native. Enforceable IP assignment built into every contract.
- Timezone: CET/EET — 1 to 3 hours ahead of UK, 6 to 9 hours ahead of US East Coast. Full daily overlap with Western Europe; partial overlap with US mornings.
Typical stack
Python, PyTorch, TensorFlow, scikit-learn, XGBoost, MLflow, Kubeflow, Weights & Biases, feature stores (Feast, Tecton), Apache Spark, AWS SageMaker, Azure ML, Vertex AI, Docker, Kubernetes, DVC.
Most engineers on our bench have worked across at least two cloud providers and have production MLOps experience — not just notebook-to-model work.
ML engineer vs AI developer — which do you need?
ML engineer: builds, trains, and deploys the model. Owns architecture decisions, training pipelines, feature engineering, and production performance.
AI developer: integrates existing models or APIs into products. Builds the application layer that calls GPT-4, Claude, or a fine-tuned model your team provides.
Some projects need both. Most projects start with one and bring in the other later. The AI Engagement Estimator helps you scope which roles you need and at what seniority.
Why Eastern Europe
Cost: senior ML engineers in Romania, Bulgaria, Poland, and Croatia cost 60–70% less than equivalent US hires on a loaded-cost basis. The saving is not on paper — it shows up directly in your monthly invoice.
Seniority: Eastern European ML engineers tend to have backgrounds in math, statistics, or CS fundamentals, not bootcamp-to-model-training pipelines. The senior cohort (7+ years) is deep.
Legal: EU jurisdiction. GDPR compliance is baseline, not a bolt-on. IP assignment under EU directives is enforceable. Work-for-hire contracts hold.
Timezone: CET/EET means 8–10 hours of working day overlap with Western Europe and 3–5 hours with US East Coast mornings. Engineers are available for standups, PR reviews, and incident response during your core hours.
Sample profile
ML engineer — 8 years experience
Stack: PyTorch, MLflow, AWS SageMaker, Feast, Python
Built a recommendation engine for an e-commerce client (2M SKUs, real-time serving under 80ms p99). Migrated batch retraining pipeline to SageMaker Pipelines, cutting retraining time from 14 hours to 3.5 hours. Led MLOps standardization across a 6-person ML team.
Location: Cluj-Napoca, Romania. Available: 2 weeks.
Related pages
Ready to hire?
Tell us the role, the stack, and the timeline. We’ll send back a cost estimate and 2–3 matched profiles within 48 hours.