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 using machine learning
• Solid understanding of machine learning fundamentals: supervised learning, feature engineering, model evaluation, overfitting, regularization, and 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
• English level sufficient 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) and probabilistic modeling or Bayesian methods
• Hands-on experience with production ML systems, including MLOps, monitoring, and 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 modeling 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

We offer

• Projects for clients such as PayPal, Wargaming, Xerox, Philips, Adidas, and Toyota
• Competitive compensation based on qualifications and skills
• Career development system with clear skill qualifications
• Flexible working hours aligned to your schedule
• Remote-work options