ML Middle/Senior Engineer (Trading)
⚲ Warszawa, Wrocław
Do uzgodnienia
Wymagania
- AWS Sagemaker
- MLflow
- SQL
- ML systems
Opis stanowiska
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 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
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
We offer:
• Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota
• Competitive compensation that depends on your qualification and skills
• Career development system with clear skill qualifications
• Flexible working hours aligned to your schedule
• Options to work remotely
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
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
We offer:
• Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota
• Competitive compensation that depends on your qualification and skills
• Career development system with clear skill qualifications
• Flexible working hours aligned to your schedule
• Options to work remotely
🔍 Dekoder Ogłoszenia
🔴
contributing to alpha generation and risk modeling
Twoja praca będzie miała bezpośredni wpływ na generowanie zysków i zarządzanie ryzykiem, co może oznaczać dużą presję i odpowiedzialność.
🔴
Ownership of tasks from research to implementation
Będziesz odpowiedzialny za cały cykl życia projektu, od pomysłu po wdrożenie, co może oznaczać dużą samodzielność, ale też potrzebę radzenia sobie z wieloma aspektami.
🟡
Ability to clearly communicate findings and trade-offs
Oczekuje się, że będziesz potrafił wyjaśniać złożone kwestie techniczne i biznesowe, co może wymagać umiejętności prezentacji i negocjacji.
🟢
Curiosity and willingness to explore new approaches
Firma ceni innowacyjność i chęć uczenia się, co może oznaczać, że będziesz miał przestrzeń na eksperymenty, ale też że nie zawsze będą istnieć gotowe rozwiązania.
🟡
Level of English enough for efficient technical and business communication with native speakers
Oczekiwany jest bardzo wysoki poziom biegłości językowej, umożliwiający swobodną komunikację w kontekście technicznym i biznesowym.