ML Engineer
What the work looks like, the stacks behind the title, what each level means and the training that helps, then how interviews run and how to prepare for each step.
What an ML engineer actually does
An engineer who builds machine learning models and puts them in production, where they run, get monitored and improve over time.
An ML engineer builds machine learning models and the systems that run them in production. They prepare the data and the features, train and evaluate the models, then deploy them so that a product or a team can use their predictions.
The work goes on after the launch. ML engineers monitor the models in production, retrain them when the data changes and measure their impact with A/B tests, together with data scientists, data engineers and product teams.
More and more teams also work with AI coding tools such as Claude Code, Cursor or GitHub Copilot, and expect engineers to use them with judgment.
- Training and evaluating a new version of a model
- Building features with data engineers
- Deploying a model or fixing a problem in production
- A review of model metrics and A/B test results
- Code reviews, tests and documentation
The same title, very different stacks
Most ML engineering searches are defined first by the use case the models serve, then by the ground every ML engineer shares.
What each level means in practice
Titles vary from one company to the next. What changes from one level to the next is the scope you own and the decisions you take.
- Works on models designed by a senior engineer
- Learns the data, the tools and the deployment process
- Writes tested code with code review
- Trains and deploys models on their own
- Monitors models and fixes problems in production
- Works directly with data scientists and product teams
- Designs ML systems for a product area
- Takes choices on models, data and serving
- Mentors other engineers and leads code reviews
- Sets the ML architecture across teams
- Leads choices on tools, platforms and standards
- Writes the design documents others build on
The paths into the role, and what really counts
Companies look first at models you have put in production and the impact they had. Education and certifications help most early in a career, or to change field.
- Engineering school or master's in computer science or AIThe most common path in France, and often expected by large companies.
- University degree in computer scienceThe most common path in the US, at master's or PhD level for research-heavy teams.
- Data science firstMany ML engineers start as data scientists and move closer to production.
- Backend or data engineering firstBackend and data engineers who learn machine learning also move into the role.
Our view: for this role, a model you put in production and can walk through, from the data to the measured impact, often says more than any certificate.
A conversation with a senior tech recruiter
Your first interview is with one of our senior tech recruiters, who knows the role, the stacks and the levels in this guide. The conversation covers your own work through real examples, and the technical exercises come later, with the company. Before the call, we send you the six themes we will cover so you can prepare your examples.
- What you owned yourselfThe models or ML systems you were responsible for, and your own part in the team's work.
- Your environment and its constraintsData quality, latency, volume, budget or rules on data: what shaped your technical choices.
- The decisions you madeWhich model, which features and how to serve it, the options you compared and the reasons for your choice.
- The impact of your workWhat changed thanks to your work, with numbers when you have them: model metrics, A/B test results, latency or costs.
- How you work with other teamsYour work with data scientists, data engineers and product teams, and how you settle a disagreement.
- What you want nextThe kind of models, use cases and scope you are looking for in your next role.
- Models in production
- Monitoring and retraining
- Measured impact
- Engineering quality
The same ground for every candidate. For a given role, every candidate answers the same final questions, built with the hiring manager around what the role really needs.
Ready for the next steps. After our call, we tell you how the company runs its interviews, who you will meet at each step and what each step is meant to assess.
What the company will assess, step by step
Each company runs its own process, and we walk you through its steps before you start. ML engineering processes often combine the steps below, and the depth expected at each step grows with your level.
- A coding exerciseLive coding or a take-home in Python, on algorithms or on a small ML task such as a data transformation or a metric.
- An ML fundamentals interviewQuestions on models, training, evaluation and common problems such as overfitting or data leakage.
- An ML system designA system to design from the data to production, such as a recommendation or a fraud model, with features, serving and monitoring.
- Meeting the manager and the teamHow you work, how you take decisions with others and what the team expects in your first months.
- ConfirmedYou solve the exercise cleanly, explain the basics of your models and ask the right questions about the data.
- SeniorYou lead the design, weigh model quality, latency and cost, and point out risks and limits before you are asked.
- Staff and PrincipalYou run most of the interview as a peer, think about the whole ML platform and connect the design to the needs of several teams.
How to prepare as an ML engineer
Practical advice for each step of an ML engineering process, from the way you introduce yourself to the questions you ask at the end of each interview.
Start with the models you put in production, their use case and your part in them. Mention your stack in one sentence, then close with what you want next.
For each one, prepare the use case, the data, your decision, the options you set aside, the result and what you would change today. Say "we" for the team and "I" for your own part.
Interviewers often check the fundamentals at every level. Review evaluation metrics, overfitting, data leakage and the trade‑offs between common models.
In an ML system design, start with the goal and the data, then the features, the model, the serving and the monitoring, in that order.
Prepare the offline metrics of your models and the business impact they had in production, and explain the gap between the two.
Rules on AI coding assistants differ from one company to the next, and sometimes from one step to the next. Ask before each step, and when they are allowed, explain what you ask the tool and how you check its output.
- Walk us through a model you put in production, from the data to the impact.
- How do you know when a model needs retraining?
- How would you design a recommendation system for our product?
- Your model works offline and fails in production. What do you check?
- Which models are in production today, and what do they serve?
- How do ML engineers and data scientists share the work?
- Which tools does the team use to train, track and deploy models?
- How does the team monitor models and decide to retrain them?
- How does the team measure the business impact of a model?
- How does the team use AI coding tools day to day?
Where ML engineers come from, and where they go
The roles people most often hold before this one, and the ones they most often move to.