Middle ML Engineer

Required for this role

Bachelor's degree or equivalent in computer science, mathematics, engineering, or a related field, with coursework in machine learning or statistical learning.

Strong Python and PySpark skills, with the ability to write clean, tested, maintainable code.

Hands-on experience with a cloud data platform such as Databricks, Snowflake, Fabric, or similar, together with strong SQL, including window functions and multi-table joins.

Solid understanding of core ML concepts, including cross-validation, overfitting, class imbalance, data leakage in time-ordered data, and selecting evaluation metrics appropriate to the problem.

Hands-on experience with gradient-boosted trees, including XGBoost and LightGBM, as well as logistic regression, support vector machines, and k-nearest neighbors.

Hands-on experience with clustering methods, including k-means and other approaches.

Experience with some of the following: survival / time-to-event analysis, experiment design and causal inference, simulation and Monte Carlo methods, probability calibration, Bayesian or hierarchical modeling, and model monitoring and drift detection.

Experience taking a model from development into a scheduled or production environment, including experience with Docker, CI/CD, and workflow orchestration.

Ability to explain model behavior, including feature importance, calibration, and limitations, and present technical results clearly to a non-technical audience.

Fluent spoken English (B2)

Nice to have

Proven experience as a machine learning engineer or in a similar role.

Experience in fintech, trading, or financial services.

Your responsibilities

Take loosely framed business problems and develop appropriate ML solutions from scratch.

Explore data, test hypotheses, and iterate on potential approaches in notebooks.

Engineer features and build models using gradient boosting, logistic regression, survival analysis, or hierarchical methods, depending on the problem.

Select rigorous, relevant evaluation metrics based on the business objective and modeling approach.

Validate whether models hold up against the available data and intended use case.

Translate experimental findings and model results into clear, actionable outcomes for the business.

Challenge problem framing when assumptions do not hold, and communicate questions, risks, or blockers early.

What you get

Your time off

18 paid vacation days and 10 paid sick days annually

10 Ukrainian public holidays

Maternity and paternity leaves

Marriage and Parenthood Package

Additional leave for major life events

Sabbatical leave opportunities

Learning & growth

Sombra University workshops and internal learning programs

Tech Communities and knowledge sharing sessions

Language courses and workshops

Mentorship opportunities

Health & well-being

Sports compensation or health insurance coverage

Participation in races and marathons

Corporate doctor (telemedicine)

Well-being initiatives and workshops

And even more

Company-provided technical equipment

PE administration and tax support

Internal referral program

IT Club loyalty program

Company events and volunteering initiatives

Before you apply

Our recruitment team will carefully review your profile, and if we see a good match with the role, we'll reach out to you shortly.

If you don't hear from us within 5 business days, it means we've decided to continue the process with other candidates for this position. Thanks for understanding.