An ML engineer trains models on data only your company has

Usually the ML engineer, if no model exists yet. The first job is finding out whether your data supports a model at all, and that is training and evaluation work. If a model already exists and nobody can retrain, serve or monitor it, start with MLOps instead. A senior ML engineer can put a first model into production; a separate MLOps role pays off once several models need retraining.

Sometimes, and the engineer checks before training anything. A hosted model is quick to try and needs no labelled data. A trained model tends to win when the task is narrow, the volume is high, the data cannot leave your systems or the answer must be the same every time. The first weeks score both on your own examples, so the choice rests on results, not preference.

Less than people fear for a narrow task, more than they hope for a broad one. The engineer starts by training a simple model on what you already have and showing where it fails. A learning curve then shows whether more labelled examples, a pretrained model or a narrower question would help most, so your experts label only what moves the score.

On the same test data, with the metric fixed before training starts. A slice of your data is never trained on, so a model cannot pass by memorising it, and where the model predicts the future that slice is the latest period. The candidate, today’s rule or model and a simple baseline are all scored on it, and the result is broken down by segment.

Only if that is measured. The engineer agrees with you which groups matter, such as regions, languages or customer types, and reports errors for each one beside the overall score. A model that looks good on average but fails one group goes back to data and features before anyone relies on it, and the gap is written into the model card.

Both, for a first model. The engineer exports the model with its preprocessing and puts it behind a prediction service or a scheduled batch job your product can use. What changes at scale is the operating work: retraining pipelines, a model registry, drift alerts and GPU cost. When several models need that, an MLOps engineer takes it over from a model card and written retraining steps.

About your labels, before any model architecture. Expect questions on who labelled the data and how disagreements were settled, how the data was split, what the business does with a wrong prediction and what rule the model has to beat. A candidate who reaches for a bigger model before asking those has usually not shipped one that people relied on.

Then you hear it early, in writing, with what would change the answer: more labelled examples, a narrower question, a data source you do not collect yet, or a plain rule that does the job. Saying no is part of the role. A model that only beats the baseline on a lucky split costs more to run than it returns, and we will not hand you one.

Yes. Many clients start with one senior engineer to cover a specific capability or capacity gap, then add specialists only when the roadmap requires them.

Yes. Adding an engineer follows the same route as the first, and you interview them. Reducing the team follows the notice period agreed in the contract, and the work is handed over in your repository before anyone leaves.

Yes. You manage priorities and day-to-day work directly, in your own channels and delivery process.

Yes. You interview every candidate using your own process and make the final decision about who joins the team.

Our engineers are ImproveIT employees and long-term contractors working through our teams in Poland and Ukraine. We are not a freelance marketplace and do not resell developers from other vendors.

Replacement terms are defined in the contract upfront, so it does not become a negotiation.

You do. IP assignment is included in the contract, and we can sign an NDA before discussing the project.

We sign an NDA before discussing the project, and confidentiality and IP assignment are part of the contract. Engineers work in your repositories, cloud accounts and tools with the access your team grants, so you can narrow or revoke it at any time.

Yes. Our engineers work with teams across Europe and the US. We agree the required working-hour overlap before matching candidates.

Our developers work in English with international product and engineering teams every day. English level is included in the candidate information you receive before the interview.

Rates depend on the role, seniority, experience, and engagement model. You receive the rate, availability, and candidate profile in writing before making any commitment.

Both are agreed before the engagement starts and included in the proposal and contract. We are flexible in that and usually agree on the terms suitable for a client.