Staff ML Platform Engineer - Fintech Engineering (m/f/n)

InPost Pay is building its own fintech platform from the ground up as a large-scale, international initiative. The company is creating a lean, highly effective engineering organization in Poland with AI-native engineering at its core.

• 7+ years in machine learning engineering in production systems.
• 3+ years experience with credit-scoring, risk modelling or decisioning ML on Spark/Databricks or equivalent lakehouse.
• Expert in Python and ML frameworks (scikit-learn, XGBoost, PyTorch) with 7+ years experience.
• Strong knowledge in credit-risk and underwriting model design including scorecards, gradient boosting, survival/behavioural models.
• Skilled in model validation methodologies such as back-testing, champion/challenger testing, population stability (PSI), and calibration.
• Experienced in point-in-time-correct feature engineering, feature-store usage, and MLOps tooling (MLflow, CI/CD).
• Fluent in explainability and responsible-AI techniques (SHAP, model cards, adverse-action reasoning).
• Experienced with agentic coding tools and loop and graph engineering with deep knowledge of LLM behavior.
• Staff-level engineer capable of setting standards and mentoring through code and design review.
• Fluent in English (B2+).

Nice to have:

• FinTech, lending, or payments ML experience.
• Model-risk management or regulatory model validation.
• MLOps or feature-store platform ownership.
• Fraud-detection modelling experience.
• Real-time, low-latency model serving.
• Board- or regulator-facing model documentation and audit experience.
• Causal inference or uplift modelling for credit-policy experimentation.

• Set the modelling, validation, monitoring, and responsible-AI/model-risk practices for credit-scoring and MCA merchant-underwriting models.
• Own the end-to-end model lifecycle including feature selection, training, validation, deployment, and post-production monitoring.
• Partner with Data Engineering to define point-in-time-correct, production-grade features and shape the feature-store roadmap.
• Establish documentation, model cards, and adverse-action reasoning standards for regulatory compliance.
• Own the architecture of agentic harnesses and loop/graph agent workflows bound to the feature store.
• Design agent loops and graphs to ensure reliability, observability, and cost control.
• Codify agentic patterns as reference for the function.
• Set technical direction, review designs and code, and mentor Senior and Mid ML Engineers.