ML engineer in fintech

InPost Pay is building its own fintech platform from the ground up. It’s a large-scale, international initiative where engineers will have the opportunity to shape the architecture, build core fintech components, and create solutions designed to operate at InPost’s scale.

• You own agentic model iteration (experiment, evaluate, retrain, promote).
• You also own agentic model monitoring and observability (drift, stability, performance), built on the feature store.
• You design the agent loop and graph and keep it reliable and cost-controlled.
• You own agentic validation and backtesting.
• You build agent loops that replicate and stress-test models and produce evidence packs for independent review.
• You also own agentic model monitoring and observability for validation.
• Build and ship credit-scoring and merchant-underwriting models end-to-end: feature engineering, training, validation, deployment and post-production monitoring.
• Engineer point-in-time-correct features against the shared feature store for BNPL and MCA decisioning.
• Partner with Data Engineering on source contracts and freshness SLAs.
• Design and operate the agent loop and graph (context, tools, evals, guardrails) so agentic delivery is reliable, observable and cost-controlled.
• Deploy and operate models with MLOps tooling: versioning, CI/CD for models, and champion/challenger or shadow deployment.
• Monitor production models for drift, stability and performance degradation.
• Close the loop with retraining, recalibration or rollback when thresholds are breached.
• Design models to be explainable and regulatory-defensible by construction.
• Document features, decisions and validation evidence to support CCD2 creditworthiness assessment and audit review.
• Model Validation track: build and run the validation and backtesting harness inside the MLOps pipeline.
• Produce audit-ready evidence packs for independent review.
• Model Lifecycle track: build the experiment, evaluate, retrain and promote loop on the model registry and serving layer.

• Someone who loves building ML systems that hold up in production and under audit.
• Hands-on first. You design and you build, with AI as a daily tool.
• Experienced in applied ML at scale, covering the end-to-end lifecycle: feature engineering, training, validation, deployment and monitoring.
• Skilled in model validation methodology: backtesting, population stability (PSI), discrimination (Gini/KS), calibration.
• Experienced in MLOps and production monitoring: model versioning, CI/CD, champion/challenger and shadow deployment, drift detection and automated alerting.
• Skilled in point-in-time-correct feature engineering and feature-store consumption patterns.
• Familiar with explainability techniques (SHAP/LIME) and documentation for regulatory defensibility.
• Fluent in Python and SQL, with Spark/Databricks for large-scale model training and serving.
• Experienced with advanced agentic tooling and loop and graph engineering: context and tool design, guardrails, eval design with auditable outputs.
• You also have solid working knowledge of LLMs.
• A collaborator who follows shared patterns and raises the bar through influence.
• Fluent in English (B2+).
• Будет плюсом: Hands-on model validation or independent-review experience; CCD2 / consumer-credit regulatory requirements for creditworthiness assessment; Merchant cash advance (MCA) underwriting and merchant-risk data; Fraud-detection modelling (e.g. bust-out, first-party fraud); IFRS 9 provisioning / expected-credit-loss modelling; Real-time, low-latency model serving

• Greenfield, high stakes.
• Strategic impact on how AI-native ML is done across InPost.
• A modern stack and a lean, AI-native team. You have space to experiment with new technologies, including AI tooling, and bring them into production.
• A collaborative, fast-moving environment where autonomy and accountability go hand in hand.
• Hybrid, ideally one day a week in the Warsaw or Kraków office.
• We offer a B2B Contract.