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Senior Machine Learning Product Engineer, Risk and Fraud Management, Payments, Growth Alliance

Warszawa, Masovian Voivodeship, Poland

Open 5 months, first seen by us 152 days ago
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Open 5 monthsWe first saw this posting 152 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records
Live
First seen by us
Jun 6, 2026152 days ago
Recorded sightings
266
Last seen by us
Nov 5, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 152

Up longer than most. Similar jobs usually come down between day 6 and day 54.

Well past HelloFresh’s usual: its jobs come down by about day 49.

From 11,069 similar postings that came down, and 33 of HelloFresh’s. Coming down is not always a hire.

HelloFresh is hiring

13 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Hiring is picking up
  • Takes jobs down at a usual pace
  • 15% of open jobs were reposted
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • AWS
  • Databricks
  • Kafka
  • Prefect
  • Terraform
  • Airflow
  • PySpark
Where the posting names them
- Production ML track record: A solid background (5+ years) taking machine learning systems from prototype into high-traffic, low-latency cloud environments. - Strong distributed data foundation: Deep command of Python, SQL, and PySpark on Databricks, paired with hands-on containerized and serverless model deployment. - Cloud & MLOps proficiency: Hands-on familiarity with cloud environments (AWS), containerization, and modern orchestration tools (such as Databricks Model Serving, Airflow, Kafka, or Terraform) to maintain event-driven pipelines. Proven capability building automated CI/CD pipelines.
- Strong distributed data foundation: Deep command of Python, SQL, and PySpark on Databricks, paired with hands-on containerized and serverless model deployment. - Cloud & MLOps proficiency: Hands-on familiarity with cloud environments (AWS), containerization, and modern orchestration tools (such as Databricks Model Serving, Airflow, Kafka, or Terraform) to maintain event-driven pipelines. Proven capability building automated CI/CD pipelines. - Pragmatic product mindset: Sound literacy in model evaluation (precision/recall balance, drift, A/B testing) combined with a bias for shipping and clear communication across disciplines.
About the role: What's in the Box As a Senior Machine Learning Product Engineer in our Risk and Fraud Management squad, you will own the production machine learning systems that protect HelloFresh from checkout fraud and intelligently recover failed recurring payments across our global markets. You will take predictive models from initial concept to high-availability, low-latency scoring systems on Databricks, directly balancing risk mitigation against a frictionless customer experience. In our AI-first engineering environment, you will drive core architectural milestones - such as our Smart Payment Retry engine - while leveraging AI coding agents to accelerate development, deployment, and testing. Ultimately, your work will protect revenue and ensure reliable, fair decisioning for millions of active customers. At HelloTech, flexibility and cross-functional collaboration are core to how we work. While this role is aligned to a specific Alliance, strong candidates may also be considered for opportunities across different teams or projects.
- 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 modern orchestrators (Airflow/Prefect) to support checkout scoring and automated payment recovery. - Drive key platform migrations, transitioning feature stores and model serving to Databricks to guarantee online/offline consistency and point-in-time correctness.
- Build and optimize data pipelines and inference workflows using PySpark on Databricks, Kafka, and modern orchestrators (Airflow/Prefect) to support checkout scoring and automated payment recovery. - Drive key platform migrations, transitioning feature stores and model serving to Databricks to guarantee online/offline consistency and point-in-time correctness. - Lead the technical delivery of our Smart Payment Retry engine, establishing the experimentation framework and rolling out ML-driven charge re-attempt policies across international markets.

Your next step

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Demand for Data Engineer roles

New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.

Weekly new Data Engineer postings at the same employers, from the week of August 17 to the week of October 26. The fewest in a week was 839, the most 1,314, and the latest week had 1,086.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 5 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under Data Engineer, by the work the posting describes.

Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.

Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.

An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.

US government figures for database architects

Median yearly pay, May 2025
$139,500
Expected change, 2025 to 2035
9.4% more jobsagainst 3.5% for all jobs

The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

Work with HelloFresh in Warsaw and its HelloTech organisation, HelloFresh’s global technology backbone with more than 1000 people, building the digital products that power our end-to-end food experience. From meal kits and ready-to-eat meals to specialty offerings like pet food and premium meat & seafood, HelloTech creates the platforms that bring tailored food solutions to millions of customers every month.

Our subscription-based, direct-to-consumer model relies on technology at every step, from customer-facing apps and personalization logic to pricing, forecasting, supply chain optimization, and initiatives that help reduce food waste. While our brands operate independently to serve distinct customer needs, they are united by shared platforms, data, and operational excellence built by HelloTech.

HelloTech works in autonomous, cross-functional alliances, each owning a specific product or domain end to end. By working with our Warsaw office, you will help shape scalable, data-driven products used across our markets, working with a modern tech stack and international teams to continuously improve how people discover, order, and enjoy HelloFresh’s products, today and in the future.

About the role: What's in the Box

As a Senior Machine Learning Product Engineer in our Risk and Fraud Management squad, you will own the production machine learning systems that protect HelloFresh from checkout fraud and intelligently recover failed recurring payments across our global markets. You will take predictive models from initial concept to high-availability, low-latency scoring systems on Databricks, directly balancing risk mitigation against a frictionless customer experience. In our AI-first engineering environment, you will drive core architectural milestones - such as our Smart Payment Retry engine - while leveraging AI coding agents to accelerate development, deployment, and testing. Ultimately, your work will protect revenue and ensure reliable, fair decisioning for millions of active customers.

At HelloTech, flexibility and cross-functional collaboration are core to how we work. While this role is aligned to a specific Alliance, strong candidates may also be considered for opportunities across different teams or projects.

What you’ll do: The Recipe

At HelloFresh we are moving away from a model where software developers just execute tickets toward one where product engineers are trusted to own customer problems. A Product Engineer takes a problem, forms a point of view, validates it with customers and data, and ships it using AI as a force multiplier. 

  • 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 modern orchestrators (Airflow/Prefect) to support checkout scoring and automated payment recovery.
  • Drive key platform migrations, transitioning feature stores and model serving to Databricks to guarantee online/offline consistency and point-in-time correctness.
  • Lead the technical delivery of our Smart Payment Retry engine, establishing the experimentation framework and rolling out ML-driven charge re-attempt policies across international markets.
  • Establish modern MLOps practices, including CI/CD automation, model registry versioning, automated validation gates, and infrastructure as code.
  • Implement automated monitoring and experimentation plumbing—from drift detection to shadow deployments and kill switches—to catch regressions before they reach customers.
  • Collaborate cross-functionally with data scientists to pressure-test models for production readiness while mentoring teammates on system design and AI-augmented engineering workflows.
  • Work beyond your specialization when the problem demands it. Your specialization is your anchor, not your boundary.
  • Operate what you build. You instrument, monitor, and improve your systems in production. Shipping is the beginning of the learning cycle, not the end.

What you’ll bring: The Ingredients 

  • Hands-on experience working with AI tooling (e.g., Claude Code, Cursor, Copilot) beyond casual experimentation. You use AI agents every day. You have a practical sense of how the context you provide to AI tools shapes output quality, and how to set boundaries on AI-generated work.
  • Production ML track record: A solid background (5+ years) taking machine learning systems from prototype into high-traffic, low-latency cloud environments.
  • Strong distributed data foundation: Deep command of Python, SQL, and PySpark on Databricks, paired with hands-on containerized and serverless model deployment.
  • Cloud & MLOps proficiency: Hands-on familiarity with cloud environments (AWS), containerization, and modern orchestration tools (such as Databricks Model Serving, Airflow, Kafka, or Terraform) to maintain event-driven pipelines. Proven capability building automated CI/CD pipelines.
  • Pragmatic product mindset: Sound literacy in model evaluation (precision/recall balance, drift, A/B testing) combined with a bias for shipping and clear communication across disciplines.
  • Deep data/ML engineering expertise, experience operating models or data products in production, and the statistical literacy to design sound experiments and interpret their results.
  • You take full ownership. You have a bias to ship. You finish the last twenty percent. 
  • You have product sense and are opinionated about what should be built and why, and you can back that opinion with data and user evidence.

Above all, we are looking for individuals who will make HelloFresh better. We believe there are many different ways of developing skills and we love diverse experiences! So even if you don’t “tick all the boxes” but think you’d thrive in this role, we would really like to learn more about you.

What we offer: The Toppings

  • Global collaboration at scale: Collaborate with experienced engineers and product partners across HelloTech’s international teams, in a culture of active knowledge sharing.
  • Technology with real-world impact: Build and operate modern systems at global scale, supporting 6+ millions of customers and complex supply chain operations.
  • Technical/Product/Design leadership: Drive best practices and influence architecture/design, quality, and ways of working in an autonomous, product-led setup.
  • End-to-end development/delivery: Drive decisions from problem definition to production, improving systems and enabling long-term scalability.
  • Access to workspace at Warsaw centre (Prosta 20). The hub offers modern facilities including showers, breakout zones, outdoor space, cycle parking, and refreshments (coffee, soft drinks, and fruit).

Are you the missing ingredient? If this sounds like a tasty opportunity, we’d be excited to hear from you.  We aim to review your profile and respond within 5 business days.


#Engineering

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Warszawa, Masovian Voivodeship, Poland

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