ML Middle/Senior Engineer (Trading)

We are looking for a Machine Learning Researcher to design, develop, and evaluate predictive models for financial markets. You will work at the intersection of quantitative research, machine learning, and real-world trading constraints, contributing to alpha generation and risk modeling.

Requirements:3+ years of relevant experience

Strong Python skills and experience with ML ecosystems (AWS Sagemaker, MLFlow)

Hands-on experience working with tabular/time series data with usage of ML Solid understanding of machine learning fundamentals: Supervised learning, feature engineering, model evaluation; Overfitting, regularization, cross-validation

• Knowledge of statistical methods and probability theory
• Experience with experiment design and offline evaluation
• Ability to work with large datasets and build efficient data processing pipelines
• Familiarity with SQL and data querying
• Strong analytical and problem-solving mindset
• Ability to clearly communicate findings and trade-offs
• Ownership of tasks from research to implementation
• Curiosity and willingness to explore new approaches
• Level of English enough for efficient technical and business communication with native speakers
• Nice to have:

Experience in financial machine learning, quantitative finance, or trading systemsknowledge of signal generation, alpha research, portfolio construction or risk modeling

Experience with: Deep learning for tabular/time series data (Transformers, RNNs, etc.); Probabilistic modeling or Bayesian methods

Hands-on experience with production ML systems (MLOps, monitoring, retraining)

Ability to define research direction and identify high-impact opportunities

• Decision-making under uncertainty
• Ability to translate business problems into ML solutions
• Responsibilities:

Develop and validate machine learning models for financial time series and cross-sectional data

• Conduct research on alpha signals, feature engineering, and predictive modelling techniques
• Design experiments and backtesting frameworks with proper statistical rigor
• Work with large-scale structured and unstructured financial datasets
• Collaborate with engineering teams to deploy models into production pipelines
• Analyze model performance, stability, and robustness under changing market conditions
• Improve data pipelines, labeling strategies, and evaluation methodologiesWe offer:

Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota

• Competitive compensation that depends on your qualification and skills
• Career development system with clear skill qualifications
• Flexible working hours aligned to your schedule
• Options to work remotely