Hire Senior Machine Learning Engineers in Europe
Engineers who ship ML and LLM systems in production - the gap between 'notebook works' and 'serving 10M requests reliably' is where they live. Modern stack, cost-aware, and allergic to hype-driven architecture.
Engineers who actually fit the craft.
Every machine learning engineers we place has been screened for the specific skills that matter in this discipline - not generic "software engineering" experience repackaged.
Production-first
Has shipped ML or LLM features to real users. Knows what model monitoring looks like, why drift is dangerous, and how to fail gracefully when the model is wrong.
LLM-fluent (not just hype)
Practical experience with OpenAI, Anthropic, Hugging Face, and open models. Knows when to fine-tune, when to use RAG, and when not to use an LLM at all.
Cost-aware
Token budgets, batch inference, distillation, quantisation, caching. Knows how to ship a useful model without bankrupting the company on API costs.
Data-literate
Upstream data quality is half the battle. Our ML engineers work well with data engineers and can hold their own on schema, labelling, and evaluation design.
Engineers in our network. Real people. Long-term.
Every engineer Talzy places is a full-time, locally-employed team member - working exclusively for one company. Not a marketplace, not a rotation.
Every modern ml / ai stack, represented in the network.
Sourcing is stack-aware - the shortlist you see only includes engineers with production experience in the technology you specify.
ML frameworks
LLM tooling
MLOps
Data & vector
Inference & infra
Evaluation
The moments a dedicated ml / ai actually earns their seat.
Your first ML / AI feature
You have decided to ship your first AI feature - summarisation, classification, a copilot. You need someone who has done this before, who will push back on bad ideas (yes, sometimes the correct answer is 'do not use an LLM here'), and who can wire it to production with reasonable costs.
Scaling a prototype to production
The demo worked in a notebook. Now you need it to serve thousands of users with acceptable latency, cost, and correctness. This is a real ML engineering problem - not a notebook-authoring exercise. Senior MLE with deployment experience required.
Model cost optimisation
Your OpenAI bill is $X and nobody is sure which feature generates most of it. A Talzy ML engineer with cost-aware deployment experience can audit usage, identify unnecessary calls, introduce caching and batching, and move the right workloads to open models where it makes sense.
RAG / retrieval system build
You need LLMs grounded in your own data. A proper retrieval system - chunking strategy, embedding model choice, vector DB, re-ranking, evaluation - is a solved problem for someone who has built one, and a disaster for someone who has not.
One brief, one shortlist, one machine learning engineer on your team.
Tell us what you need. We come back in 3–5 business days with 3–5 machine learning engineers who fit your stack, your seniority bar, and your team rhythm - already vetted, already interested.
Calculate your ml / ai cost.
Salary at-cost (no markup) + a tiered monthly management fee + a workspace fee. No recruitment fee. All shown in USD, per month and per year. Move the controls, see exactly what you will pay.
Typical monthly cost by seniority
All-in, including employment, workspace, and Talzy fee. Ranges cover our three active markets.
Ships ML features against defined product scope
Owns ML systems end-to-end, makes architecture calls
Drives ML strategy, sets evaluation standards
The bar a ml / ai clears before you meet them.
Technical skill is table stakes - alignment, stability, and communication matter just as much. We screen for all four before anyone lands on your shortlist.
Production ML walkthrough
Walk us through a model you have shipped. Training, deployment, monitoring, drift. Notebook-only profiles get flagged here.
System design for ML
A realistic LLM or ML feature end-to-end - ingestion, training, serving, evaluation. Tradeoff thinking, not tool-name bingo.
Cost & infra reasoning
Given a usage pattern, what would you do to keep costs sensible? Reveals whether they have operated systems with real budgets.
Evaluation design question
'How would you know if this feature is working?' The answer distinguishes real MLEs from prompt engineers.
Async communication check
A technical writeup on a real project. Can they explain complex ideas clearly in writing, for non-ML audiences?
From brief to engineer-at-desk in 2–3 weeks.
Role definition
We lock in requirements, seniority, stack, team fit, and the non-obvious things (timezone overlap, oncall, tooling).
Curated shortlist
Sourced from our active talent network across Latvia, Lithuania, and Poland. Every candidate vetted by a Talzy engineer first.
Your interviews
You run the final technical rounds. We prep candidates on your stack and handle the scheduling friction.
Employ & onboard
Local contract, payroll, and equipment ready. Engineer joins your sprint cycle on day one.
Talzy vs the other options.
A side-by-side honest comparison against the common ways to hire a machine learning engineer - marketplace contractors, in-house recruiting, and outsourcing agencies.
| Talzy | Toptal / Arc | In-house | Agency | |
|---|---|---|---|---|
| Time to first hire | 2–3 weeks | 3–6 weeks | 3–5 months | 4–8 weeks |
| Cost structure | Salary + flat fee | Hourly markup 50–100% | Fully loaded salary | 60–120% markup |
| Employment | Full-time employee | Contractor | Direct employee | Vendor staff |
| You own the relationship | Yes | Yes | Yes | No |
| Long-term retention support | Yes - career program | No | Your HR | No |
| Replacement if it fails | Included | Case by case | You re-recruit | Depends on contract |
We had two failed attempts at hiring a senior MLE before Talzy. The engineer they placed shipped our first GPT-based feature to production in six weeks - including monitoring, cost controls, and a proper evaluation harness. He is still with us.
Hiring machine learning engineers through Talzy
Ready to hire your next machine learning engineer?
Tell us the role and team context. We will send a shortlist of matching machine learning engineers from our network within 3–5 business days.