HelloFresh

Senior Machine Learning Product Engineer, Risk and Fraud Management, Payments, Growth Alliance (all genders)

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SeniorOn-site·WarszawaFull-time

#465524·Dodano 5 miesięcy temu·0

Źródło: HelloFresh

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Tech Stack / Keywords

Databricks PySpark Python SQL Kafka AirflowPrefectAWS Terraform CI/CD

Podsumowanie oferty

You will own production machine learning systems that protect against checkout fraud and recover failed recurring payments across global markets. You will build real-time scoring and payment-recovery systems using Databricks, PySpark, Kafka, and modern orchestrators.

Wymagania

Hands-on experience using AI tooling such as Claude Code, Cursor, or Copilot, including daily use of AI agents and practical experience shaping their outputs and setting boundaries on AI-generated work.
5+ years of experience taking machine learning systems from prototype to high-traffic, low-latency cloud environments.
Strong command of Python, SQL, and PySpark on Databricks, with hands-on containerized and serverless model deployment experience.
Familiarity with cloud environments such as AWS, containerization, and orchestration tools such as Databricks Model Serving, Airflow, Kafka, or Terraform; proven ability to build automated CI/CD pipelines.
Model evaluation literacy, including precision/recall balance, drift, and A/B testing, and clear communication across disciplines.
Experience operating models or data products in production, with the statistical literacy to design experiments and interpret results.
Ability to take full ownership and deliver work through to completion.
Product sense and the ability to support opinions about what should be built and why with data and user evidence.

Obowiązki

Architect and operate real-time and streaming production ML systems end to end, maintaining high availability, tight latency targets, and dependable fallback mechanisms.
Build and optimize data pipelines and inference workflows using PySpark on Databricks, Kafka, and orchestrators such as Airflow or Prefect.
Drive platform migrations of feature stores and model serving to Databricks, ensuring online/offline consistency and point-in-time correctness.
Lead technical delivery of the Smart Payment Retry engine, establish its experimentation framework, and roll out ML-driven charge re-attempt policies across international markets.
Establish MLOps practices, including CI/CD automation, model registry versioning, automated validation gates, and infrastructure as code.
Implement monitoring and experimentation systems, including drift detection, shadow deployments, and kill switches.
Collaborate with data scientists to assess models for production readiness and mentor teammates on system design and AI-augmented engineering workflows.
Instrument, monitor, and improve systems in production.

Benefity

Access to a workspace in central Warsaw with showers, breakout zones, outdoor space, cycle parking, and refreshments including coffee, soft drinks, and fruit.

Parking dla rowerów

Prysznic

Napoje w biurze

Darmowe przekąski

Inne informacje

The company aims to review applications and respond within 5 business days.

HelloFresh

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