InPost

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

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SeniorHybryda·WarszawaFull-time

#459682·Dodano 15 dni temu·1

Źródło: InPost

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Tech Stack / Keywords

Python Spark Databricks scikit-learnXGBoostPyTorch MLflow CI/CD LLMSHAP

Firma i stanowisko

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.

Wymagania

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.

Obowiązki

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.

Benefity

Architectural authority from day one.
Involvement in a greenfield, high-stakes project.
Modern technology stack with a lean, AI-native team.
Collaborative and fast-moving environment.
Hybrid work mode with office presence ideally one day a week in Warsaw or Kraków.
B2B contract offered.

InPost

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