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ML Middle/Senior Engineer (Trading)

Poland · Job Posted March 27, 2026
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Job Description

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.

Job Responsibility

  • 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 methodologies

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 systems
  • knowledge 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

What we 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
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