ML Middle/Senior Engineer (Trading)

Location: Remote, Tbilisi

Company

Work on projects for clients including PayPal, Wargaming, Xerox, Philips, adidas, and Toyota.

What you will do

• Develop and validate machine learning models for financial time series and cross-sectional data.
• Research alpha signals, feature engineering, and predictive modeling techniques.
• Design statistically rigorous experiments and backtesting frameworks.
• Work with large-scale structured and unstructured financial datasets and improve data pipelines, labeling strategies, and evaluation methods.
• Collaborate with engineering teams to deploy models into production pipelines.
• Analyze model performance, stability, and robustness under changing market conditions.

Requirements

• 3+ years of relevant experience.
• Strong Python skills and experience with ML ecosystems including AWS SageMaker and MLflow.
• Hands-on experience with tabular and time-series data using machine learning.
• Solid understanding of supervised learning, feature engineering, model evaluation, overfitting, regularization, and cross-validation.
• Knowledge of statistical methods and probability theory, with experience in 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 skills and the ability to communicate findings and trade-offs clearly.
• Ownership of tasks from research through implementation, plus curiosity and willingness to explore new approaches.
• English proficiency at B1 level or higher.

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 or time-series data, including Transformers and RNNs.
• Experience with probabilistic modeling or Bayesian methods.
• Hands-on experience with production ML systems, including MLOps, monitoring, and retraining.
• Ability to define research direction, identify high-impact opportunities, make decisions under uncertainty, and translate business problems into ML solutions.

Culture & Benefits

• Remote work options.
• Competitive compensation based on qualifications and skills.
• Career development system with clear skill qualifications.
• Medical expense compensation, online English courses, and gym membership compensation.
• Internal conferences, workshops, meetups, corporate parties, and events for employees and their children.
• Five paid sick days per year without a requirement to submit a sick-leave certificate.