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HelloFresh销售订阅制餐盒——包含食谱和预先分好份量的食材、每周配送——在18个国家开展业务，旗下品牌包括Green Chef、EveryPlate、Factor_和YouFoodz。这家柏林科技公司开发背后的软件：推荐系统（为每位顾客选择看到的食谱）、定价和订阅基础设施，以及运行物理履行中心的供应链系统，每周生产超过100万个餐盒。

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hellofresh · 柏林, 德国 · 2026 年 8 月 1 日（发布 23 天）

已经开放 23 天了。这类岗位通常在头两周就筛完人，现在投是逆风。

Senior Staff Machine Learning Engineer, Menu Personalisation (m,f,x)

这份工作

为什么是你

  • The role is about putting ML systems into production at scale: feature pipelines, training workflows, model serving, and the infrastructure under them. That is what Ruby does at GMI Cloud right now — tuning vLLM/SGLang, building an LLM proxy with dynamic routing and failover, wiring up OpenTelemetry observability, and cutting model deployment time from two hours to under thirty minutes. The work is directly transferable, even if her domain is LLM inference rather than menu personalization.
  • The posting calls for someone who can take research models to production, partner with data scientists, and own the operational reality of the system. Ruby’s Google X experience was exactly that: foundation model MLOps, leading a five-person team to deliver an MVP that accelerated labeling by 10x. She has shown she can bridge research and production, and the team structure here — Data Scientists, Data Engineers, Backend Engineers, ML Engineers — mirrors the cross-functional environment she has thrived in.
  • The role emphasizes technical leadership, mentorship, and setting engineering standards that outlast the quarter. Ruby led Project Dolphin as tech lead and has been a senior individual contributor shaping infrastructure at GMI Cloud. She has the hands-on seniority to step into a staff-level role where influence travels beyond the immediate team.

我的顾虑

  • The role is in Berlin, requiring relocation from Munich — a real cost to her. She said moving must be worth it with pay clearly above the threshold, and the posting does not disclose a salary range. Without that number, she cannot assess whether it clears the bar or compensates for the move. Also, the stack leans heavily on Go (Kafka, Kubernetes, backend services), which Ruby knows at a working level but not as a primary language. The posting asks for fluency across the Python/Spark and Go/Kubernetes stack, and a Go-heavy interview could expose the gap between ‘working knowledge’ and deep experience.
  • The explicit requirement for 8+ years building production ML systems and ‘architectural decisions that held up over multiple years’ sets a high bar. Ruby’s ML infrastructure work at GMI Cloud is strong but recent (under a year). The role demands a track record of decisions that proved out over years and influenced teams beyond one’s own — she has led teams and delivered, but the tenure of her ML-specific leadership is concentrated in the last two years. The recruiter may probe for evidence of longer-cycle architectural impact.
  • The posting does not state visa sponsorship, and HelloFresh is a German company operating in Berlin — it is likely they can sponsor, but it is not confirmed. She should ask on the first call.
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职位简介和要求

以下是启事原文 —— 中文版今晚生成。

About HelloFresh

At HelloFresh, we want to change the way people eat forever by offering our customers high-quality food and recipes for different meal occasions. Even after celebrating our 10-year anniversary, we continue to see this mission spread around the world and beyond our wildest dreams. Now, we are a global food solutions group and the world's leading meal kit company, active in 18 countries across 3 continents. So, how did we do it? Our weekly boxes full of exciting recipes and fresh ingredients have blossomed into a community of customers looking for delicious, healthy and sustainable options. The HelloFresh Group now includes our core brand, HelloFresh, as well as: Green Chef, EveryPlate, Chefs Plate, Factor_, YouFoodz, The Pets Table and GoodChop.

About the Team

Menu Personalization decides what millions of customers see when they open HelloFresh each week. The team owns the recommender systems that match customers to recipes across our global markets, and brings together Data Scientists, Backend Engineers, Data Engineers, ML Engineers, and Product to take ideas from experiment to production. The work directly shapes customer experience and business growth: when personalization gets better, customers find recipes they love faster, and HelloFresh becomes a stronger weekly habit.

At HelloFresh we are moving away from a model where software developers just execute tickets toward one where engineers are trusted to own customer problems. You take a problem, form a point of view, validate it with customers and data, and ship it using AI as a force multiplier.

About the Role

We are looking for a technical leader for the Menu Personalization ML systems, someone who owns the recommender stack that runs in production. You will set the direction for how we design, build, and operate the ML systems behind menu personalization, while staying hands-on across feature pipelines, training workflows, model serving, experimentation tooling, and the infrastructure underneath. The decisions you make here shape the platform for years, not quarters, and you will be expected to hold a point of view on where personalization at HelloFresh should go and to back it with data and user evidence. You will partner with Data Scientists to take models from notebook to production, with Data Engineers on features and pipelines, with Backend Engineers on online inference paths, and with Product on what to build next. Your influence will reach well beyond Menu Personalization: through the standards you set, the architectural decisions you make, the engineers you grow around you, and the company-wide initiatives you drive to advance ML and data engineering craft across HelloFresh. 

What you'll do

• Set the technical direction for the ML systems behind menu personalization, owning the end-to-end stack: feature pipelines, training workflows, model serving, experimentation tooling, and infrastructure.

• Take research and experiments to reliable production systems, partnering with Data Scientists on services that meet real latency, scalability, and observability requirements.

• Shape the personalization roadmap with Product and Engineering leadership, backing your point of view with data and user evidence.

• Operate what you build, instrumenting and improving your systems in production because shipping is the beginning of the learning cycle, not the end of it.

• Raise the technical bar across the team through architecture reviews, mentorship, and the example you set on production ML craft.

• Shape long-term architecture and make platform decisions whose payoff plays out over years rather than quarters.

• Drive engineering excellence beyond Menu Personalization by setting standards other ML and data teams adopt.

• Sync with peers across HelloFresh on best practices and contribute to company-wide engineering initiatives that move the broader ML and data craft forward.

What you'll bring

• 8+ years building and operating production ML systems, with a track record of technical leadership at scale.

• Architectural decisions in your past that held up over multiple years and influenced teams beyond your own.

• Production experience with recommender systems or large-scale personalization is a strong plus.

• Fluency across our data and ML stack (Python, Spark) and our 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 know when a model is actually better versus when the metrics are lying to you.

• Operational judgment to diagnose system misbehavior, find root causes, and ship systems you can debug under real load.

• Hands-on experience with AI tooling (Claude Code, Cursor, Copilot) beyond casual experimentation; you use AI agents every day and have a practical sense of how the context you provide shapes output quality.

• Product sense: opinionated about what should be built and why, with the ability to back it with evidence and translate it into business value.

• A bias to ship; you take full ownership and finish the last twenty percent.

Let’s cut to the cheese, this is why you'll love it here

• Shape the future of food:  Play a key role in transforming how millions of people eat as we grow into a global food solutions company.

• Drive transformational change:  Your decisions will have a direct and immediate impact, helping to shape strategy, scale teams, and redefine leadership in a rapidly evolving business.

• Build a global brand:  Create deeper, more sustainable connections with our customers, putting them at the heart of every decision.

• Benefit from long-term wealth creation:  Your compensation is heavily weighted toward equity, directly aligning your personal success with the company's future through our Virtual Stock Option (VSO) and Restricted Stock Unit (RSU) plans.

• Thrive in a high-performance environment:  Work with ambitious, intellectually curious peers on a global scale, enjoying an environment built for empowerment and speed.

• Enjoy a discount: on HelloFresh meal kits, delivered straight to your door.

Flexible Hybrid Approach

At HelloFresh, we know that flexible work arrangements are essential in enabling you to do your best work, while balancing your personal and life needs. Offering remote work flexibility, along with the opportunity to interact and collaborate in the office are all a part of creating a great employee experience. 

To meet these needs, we are pleased to provide Flexible Hybrid work. Flexible Hybrid is a people-first approach that is based on choice, trust, personalization, and empowers teams to choose when and how often they work from the office and work from home, in addition to team days and company days. This means a minimum of 2 days in office per week, with most teams in office between 2-3 days a week.