Middle/Senior ML Engineer (Trading)

Location: Remote (Belarus, Minsk)

Company

Software services company delivering projects for clients including PayPal, Wargaming, Xerox, Philips, adidas, and Toyota.

What you will do

• Develop and validate machine learning models for financial time-series and cross-sectional data.
• Research alpha signals, feature engineering, predictive modeling, and risk modeling approaches.
• Design statistically rigorous experiments and backtesting frameworks.
• Process large structured and unstructured financial datasets and improve data pipelines, labeling, and evaluation methods.
• Analyze model performance, stability, and robustness under changing market conditions.
• Collaborate with engineering teams to deploy models into production pipelines.

Requirements

• 3+ years of relevant experience.
• Strong Python skills and experience with machine learning ecosystems, including AWS SageMaker and MLflow.
• Hands-on experience with tabular and time-series data, supervised learning, feature engineering, model evaluation, regularization, overfitting, and cross-validation.
• Knowledge of statistical methods and probability theory, plus experience with experiment design and offline evaluation.
• Ability to work with large datasets, build efficient data-processing pipelines, and query data with SQL.
• English sufficient for effective technical and business communication with native speakers.

Nice to have

• Experience in financial machine learning, quantitative finance, or trading systems.
• Knowledge of signal generation, alpha research, portfolio construction, or risk modeling.
• Experience with deep learning for tabular or time-series data, probabilistic or Bayesian modeling, and production ML systems.
• Ability to define research direction, identify high-impact opportunities, and translate business problems into ML solutions.

Culture & Benefits

• Remote work options.
• Competitive compensation based on qualifications and skills.
• Career development system with clear skill qualifications.
• Medical expense and gym membership compensation.
• Online English courses, internal conferences, workshops, and meetups.
• Five paid sick days per year without requiring a sick-leave certificate, plus corporate events and sports competitions.