Machine Learning Engineer (Trading) – Mid/Senior

About the role

We are seeking a Machine Learning Engineer specialized in trading to design, develop and evaluate predictive models for financial markets. The role sits at the intersection of quantitative research, machine learning and real‑world trading constraints, contributing directly to alpha generation and risk modelling.

Key responsibilities

• Develop and validate machine‑learning models for financial time‑series and cross‑sectional data.
• Research and prototype alpha signals, feature‑engineering techniques and predictive modelling approaches.
• Design experiments and back‑testing frameworks with rigorous statistical validation.
• Process large‑scale structured and unstructured financial datasets and build efficient data pipelines.
• Collaborate with engineering teams to deploy models into production and monitor performance.
• Analyse model stability, robustness and retraining needs under changing market conditions.
• Continuously improve data labeling, pipeline reliability and evaluation methodologies.

Required profile

• 3+ years of relevant experience in machine learning or data science.
• Strong Python programming skills.
• Hands‑on experience with AWS SageMaker, MLflow and other ML ecosystems.
• Proven work with tabular and time‑series data.
• Solid understanding of supervised learning, feature engineering, model evaluation, regularisation and cross‑validation.
• Knowledge of statistical methods, probability theory and experiment design.
• Ability to work with large datasets and write efficient SQL queries.
• Excellent analytical mindset, clear communication in English and ownership from research to implementation.

Required skills

• Python
• AWS SageMaker
• MLflow
• SQL
• Tabular data handling
• Time‑series modeling
• Supervised learning techniques
• Feature engineering
• Model evaluation and cross‑validation

What we offer

• Projects for leading clients such as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota.
• Competitive compensation based on qualifications and skills.
• Clear career development path with defined skill qualifications.
• Flexible working hours and remote work options.