How to hire a machine learning engineer: skills, questions and costs
Many companies hire for machine learning before they know what they want the model to do, and that is where projects stall. This guide covers how to define the work, what to test, which questions show real experience and how to set up the first months so the model reaches production. If your plans are mainly about chatbots or features built on large language models, also look at our AI engineers; for the surrounding product code, our Python developers.
Start with the decision, not the model
Write down the decision the model should improve and how you will measure it. “Predict which customers will cancel next month so the support team can call them” is a project. “Use machine learning on our data” is not. A clear decision tells the engineer which data matters, which metric to use and how good the model has to be before it is worth shipping.
What to test
- Data skills: SQL, pandas and the judgement to spot missing, duplicated or misleading data.
- Modelling: when to use a simple linear model, a tree-based model such as XGBoost or a neural network.
- Evaluation: proper splits, avoiding data leakage and choosing metrics that match the business cost of mistakes.
- Production: packaging a model, serving it through an API or batch job, and versioning data and models.
- Monitoring: catching drift and falling accuracy after launch.
- Communication: explaining results and limits to people who do not work with data.
Questions worth asking
- Tell me about a model you put into production. What went wrong after launch?
- How would you check whether our data can support this model before training anything?
- Your model scores very well offline but users see no difference. What do you look at?
- How do you decide when a model needs retraining?
- When would you recommend not using machine learning at all?
Good answers mention concrete tools, numbers and trade-offs from their own work. A candidate who reaches for the most complex model before looking at the data is a warning sign.
Machine learning engineer or AI engineer
The two roles overlap, but the focus differs. A machine learning engineer trains and maintains models on your own data, often tabular data such as transactions, events or sensor readings. An AI engineer mostly builds product features on top of large language models through APIs, with prompts, retrieval and evaluation. Many teams need one of each as they grow.
What affects the price
Seniority matters most: an engineer who can design the whole pipeline, choose the infrastructure and lead others costs more than one who trains models inside a setup that already exists. Experience with your kind of data, such as images, text or financial records, and with your cloud platform also moves the price. At DigiWorks, the engineer’s pay and our fee are shown separately.
Choosing the right level
A junior engineer can run experiments and improve existing models with guidance. A mid-level engineer can own a model from data to deployment. If this person will be your first machine learning hire, choose a senior who has taken several models to production and can set up the pipeline, monitoring and habits the team will rely on later.
Tools you will hear about
Most machine learning work runs on Python with pandas, scikit-learn, XGBoost and PyTorch or TensorFlow. Around the models sit tools for the rest of the life cycle: MLflow or Weights & Biases to track experiments, Airflow or dbt to run data pipelines, Docker and Kubernetes or a cloud service such as SageMaker or Vertex AI to serve models. Your engineer does not need every tool, but should know one option for each step and why it fits your setup.
A short paid task
Give your final candidates the same small, paid task based on a sample of your real data, with personal details removed. Ask for a baseline, one improved model and a one-page note on results, risks and next steps. The note often tells you more than the code.
Data access and security
Plan access before day one. Give the engineer an account in your cloud with only the permissions they need, keep data in your environment, and remove personal details the model does not use. This protects your customers and avoids weeks of waiting for access.
The first three months
In the first week, share the data, the goal and any earlier experiments, and agree on the metric and the baseline. In the first month, expect a cleaned dataset, a baseline model and an honest view of what is possible. By the third month, a good engineer should have a model in production or in a live test, with monitoring and a plan for the next improvement.
Mistakes to avoid
- Starting without a clear decision or metric.
- Judging the model on the same data it was trained on.
- Shipping a model without monitoring or a way to roll back.
- Expecting a notebook prototype to run in production unchanged.
Building a wider remote engineering team? Read our remote software engineering hiring playbook.