Middle/Senior ML Engineer (Trading)

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.

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 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