Senior Machine Learning Engineer

2 tygodni temu


Warsaw, Polska Sigma Software Pełny etat

We are looking for a Machine Learning/AI Engineer to build and optimize predictive models for the “Magnificent 7” stocks (NVDA, AAPL, META, TSLA, GOOG, MSFT, and AMZN), using techniques like time-series analysis, sentiment modeling, and advanced feature engineering.

As a Machine Learning/AI Engineer, you’ll design, implement, and maintain a range of advanced AI/ML models aimed at boosting trading performance. Using up to a decade’s worth of historical market data — collected daily, every 4 hours, or hourly — you’ll ensure robust, data-driven insights that power our core strategies.

Customer
The client is a fintech company that develops AI-driven trading solutions tailored to the stock market. Our goal is to design strategies that maximize risk-adjusted returns for portfolios.

Requirements
At least 5 years of experience in Machine Learning or AI-related roles with a focus on financial data modeling, quantitative analysis, or algorithmic trading systems
Proficiency in Python, with hands-on experience using libraries such as TensorFlow, PyTorch, scikit-learn, and XGBoost
Familiarity with big data frameworks (e.g., Spark, Dask) and cloud platforms like AWS or GCP
Proven track record of developing and deploying trading models or financial strategies
Strong experience in time-series forecasting, financial data analysis, and feature engineering for stock market data, including technical indicators and sentiment analysis
Expertise in hyperparameter tuning techniques, model optimization, and performance enhancement
Solid foundation in statistics, probability, and optimization methods, with knowledge of risk management metrics such as Sharpe Ratio, Alpha, and Beta for portfolio optimization
At least an Upper-Intermediate level of English

Will be a plus
Experience in proprietary trading, hedge funds, or asset management firms
Knowledge of trading platforms such as Interactive Brokers, Alpaca, or similar systems
Knowledge of options pricing, derivatives, or quantitative trading strategies
Familiarity with alternative data sources, including news sentiment, social media trends, and other non-traditional datasets for market analysis
Experience with transformer models (both language and visual)
Hands-on experience with backtesting tools like Zipline or Backtrader
Familiarity with Docker, Kubernetes, and CI/CD pipelines for scalable model deployment

Personal Profile
Strong critical thinking and problem-solving skills, with the ability to assess and challenge model assumptions
Excellent communication skills for presenting complex concepts
Ability to work both independently and collaboratively in fast-paced, dynamic environments

Responsibilities
Collect, clean, and preprocess historical financial data, extracting meaningful features such as moving averages, RSI, and volatility indicators to enhance model performance
Design and train predictive models (e.g., LSTM, XGBoost, Random Forests) with rigorous backtesting, hyperparameter tuning, and evaluation using metrics such as Sharpe Ratio, Sortino Ratio, and Maximum Drawdown
Deploy models in production environments, monitor performance, address data drift through retraining, and collaborate with teams to integrate insights into trading systems while maintaining thorough documentation


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