# Senior Machine Learning Product Engineer, Communications ... [Hellofresh](https://gurify.com/jobs?q=Hellofresh) · Warszawa, Poland · Posted 3 weeks ago Senior [AI & ML](https://gurify.com/jobs/ai-ml) [Apply on the original posting → (opens in a new tab)](https://job-boards.greenhouse.io/hellofresh/jobs/8119649) ## Job description 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 The Communications tribe, a key part of the Growth Alliance, enables our brands to deliver highly personalized, meaningful interactions with our users across global markets. As a Senior Machine Learning Product Engineer within this team, you will drive a direct business impact by shaping the intelligence behind how, when, and what we communicate with our customers. ### Your work will primarily focus on three core initiatives: - Send Decisioning: Improving the data layer and integration for the decisioning engine, specifically utilizing the company's existing feature store and feature sets, to optimize our communication channels (email, SMS, and push notifications). Your models will automate the logic that determines the most relevant message and timing for each individual user. - Intelligent Funnel: Supporting a collaborative, cross-tribe initiative aimed at personalizing the post-click experience. You will streamline the content generation process to reduce manual work when creating email marketing copy. - AI-Assisted Content & Search: Developing automated asset retrieval to select images based on context during email creation, moving away from manual selection. Additionally, you will develop tools that assist marketing teams in the automated composition of new, high-performing communication assets. To succeed in this project, you should be a pragmatic and curious problem solver who excels in a cross-functional environment. Success involves translating complex user behavior into scalable models, identifying opportunities to improve our data foundations, and balancing technical depth with a strong focus on driving user engagement. 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. - Own & elevate ML infrastructure: Build, maintain, and refine the core repositories, developer tooling, and engineering workflows that enable the team to organize, scale, and deploy production ML systems smoothly. - Build and operate data products and machine-learning systems, taking them from experimentation through production and owning their real-world performance. - Architect the Send Decisioning data layer: Evaluate, optimize, and potentially redesign our communication platform integrations - building robust pipelines and integrating our feature store to enhance data quality and feature availability for decisioning models. - Engineer AI-assisted content & asset systems: Develop scalable integrations and workflows to streamline email copy generation (subject lines, copy context) and build context-driven asset retrieval systems for marketing teams. - Build production-grade pipelines & serving layers: Translate research and experimentation into low-latency, highly reliable production systems, ensuring continuous observability, data reliability, and error handling. - Bridge research & production: Partner closely with Data Scientists to transition experimental models into resilient, low-latency production services with high observability and robust fallback mechanisms. - Set the ML engineering standard: Build, standardize, and maintain core repositories, CI/CD templates, and ML platform practices (Databricks, MLflow) to streamline how the team develops and deploys code. Take full accountability for production reliability—monitoring input data quality, drift, and latency across critical communication and personalization funnels. - 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. - 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. - (Ideally) 5+ years of experience building and operating production ML systems. - Fluency across the data and ML stack (Python, Spark, Databricks) and working knowledge of the backend and platform stack (Go, Kafka, Kubernetes), with hands-on experience across pipelines, model serving, and observability at scale. - Statistical literacy to design honest experiments and the judgment to evaluate model metrics accurately. - Operational judgment to diagnose system misbehavior under real load, identify root causes, and deploy robust fixes. - 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 **Live in Hellofresh’s hiring system.** Read from the company's own applicant tracking system, not reposted from a job board — so it's a real, open requisition rather than an ad that outlived the role. 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From meal kits and ready-to-eat meals to specialty offerings like pet food and premium meat \u0026amp; seafood, HelloTech creates the platforms that bring tailored food solutions to millions of customers every month.\u003C/p\u003E\u003Cp\u003EOur 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.\u003C/p\u003E\u003Cp\u003EHelloTech 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\u2019s products, today and in the future.\u003C/p\u003E\u003Ch3\u003EAbout the role: What\u0026#39;s in the Box\u003C/h3\u003E\u003Cp\u003EThe Communications tribe, a key part of the Growth Alliance, enables our brands to deliver highly personalized, meaningful interactions with our users across global markets. As a Senior Machine Learning Product Engineer within this team, you will drive a direct business impact by shaping the intelligence behind how, when, and what we communicate with our customers.\u003C/p\u003E\u003Ch3\u003EYour work will primarily focus on three core initiatives:\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003ESend Decisioning: Improving the data layer and integration for the decisioning engine, specifically utilizing the company\u0026#39;s existing feature store and feature sets, to optimize our communication channels (email, SMS, and push notifications). Your models will automate the logic that determines the most relevant message and timing for each individual user.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EIntelligent Funnel: Supporting a collaborative, cross-tribe initiative aimed at personalizing the post-click experience. You will streamline the content generation process to reduce manual work when creating email marketing copy.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EAI-Assisted Content \u0026amp; Search: Developing automated asset retrieval to select images based on context during email creation, moving away from manual selection. Additionally, you will develop tools that assist marketing teams in the automated composition of new, high-performing communication assets.\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003ETo succeed in this project, you should be a pragmatic and curious problem solver who excels in a cross-functional environment. Success involves translating complex user behavior into scalable models, identifying opportunities to improve our data foundations, and balancing technical depth with a strong focus on driving user engagement.\u003C/p\u003E\u003Cp\u003EAt 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.\u003C/p\u003E\u003Ch3\u003EWhat you\u2019ll do: The Recipe\u003C/h3\u003E\u003Cp\u003EAt 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.\u003C/p\u003E\u003Cul\u003E\u003Cli\u003EOwn \u0026amp; elevate ML infrastructure: Build, maintain, and refine the core repositories, developer tooling, and engineering workflows that enable the team to organize, scale, and deploy production ML systems smoothly.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBuild and operate data products and machine-learning systems, taking them from experimentation through production and owning their real-world performance.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EArchitect the Send Decisioning data layer: Evaluate, optimize, and potentially redesign our communication platform integrations - building robust pipelines and integrating our feature store to enhance data quality and feature availability for decisioning models.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EEngineer AI-assisted content \u0026amp; asset systems: Develop scalable integrations and workflows to streamline email copy generation (subject lines, copy context) and build context-driven asset retrieval systems for marketing teams.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBuild production-grade pipelines \u0026amp; serving layers: Translate research and experimentation into low-latency, highly reliable production systems, ensuring continuous observability, data reliability, and error handling.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBridge research \u0026amp; production: Partner closely with Data Scientists to transition experimental models into resilient, low-latency production services with high observability and robust fallback mechanisms.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ESet the ML engineering standard: Build, standardize, and maintain core repositories, CI/CD templates, and ML platform practices (Databricks, MLflow) to streamline how the team develops and deploys code. Take full accountability for production reliability\u2014monitoring input data quality, drift, and latency across critical communication and personalization funnels.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EWork beyond your specialization when the problem demands it. Your specialization is your anchor, not your boundary.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EOperate what you build. You instrument, monitor, and improve your systems in production. Shipping is the beginning of the learning cycle, not the end.\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EWhat you\u2019ll bring: The Ingredients\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EHands-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.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EDeep data/ML engineering expertise, experience operating models or data products in production, and the statistical literacy to design sound experiments and interpret their results.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003E(Ideally) 5\u002B years of experience building and operating production ML systems.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EFluency across the data and ML stack (Python, Spark, Databricks) and working knowledge of the backend and platform stack (Go, Kafka, Kubernetes), with hands-on experience across pipelines, model serving, and observability at scale.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EStatistical literacy to design honest experiments and the judgment to evaluate model metrics accurately.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EOperational judgment to diagnose system misbehavior under real load, identify root causes, and deploy robust fixes.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou take full ownership. You have a bias to ship. You finish the last twenty percent.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EYou have product sense and are opinionated about what should be built and why, and you can back that opinion with data and user evidence.\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003EAbove 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\u2019t \u201Ctick all the boxes\u201D but think you\u2019d thrive in this role, we would really like to learn more about you.\u003C/p\u003E\u003Ch3\u003EWhat we offer: The Toppings\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EGlobal collaboration at scale: Collaborate with experienced engineers and product partners across HelloTech\u2019s international teams, in a culture of active knowledge sharing.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ETechnology with real-world impact: Build and operate modern systems at global scale, supporting 6\u002B millions of customers and complex supply chain operations.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ETechnical/Product/Design leadership: Drive best practices and influence architecture/design, quality, and ways of working in an autonomous, product-led setup.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EEnd-to-end development/delivery: Drive decisions from problem definition to production, improving systems and enabling long-term scalability.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EAccess to workspace at Warsaw centre (Prosta 20). 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