Senior Machine Learning Engineer

We are the platform turning browsing into shopping. We connect 200 million shoppers with deals they love while boosting local sales for hundreds of top retailers and brands. We help consumers save time and money while making smart shopping decisions, and we support retailers and brands in engaging customers from online research to in-store purchases. Ready to spark your growth with us? WHO WE LOOK FOR 🦄 We are looking for a Senior Machine Learning Engineer to join our Audience Platform team within Ad Products. In this role, you will own and extend both the machine learning systems and the corresponding backend infrastructure that power 1P, 2P, and 3P audience data across Flipp and Shopfully. This is an engineering-first role for someone who builds and operates production ML systems end-to-end—not a research scientist handing off code to someone else to productionize. You bring hands-on comfort with statistics, model selection, building, tuning, and experiment design, combined with strong software engineering fundamentals to build scalable pipelines on core tables running into tens of billions of rows. If you are passionate about MLOps, scalable feature engineering, and shipping production ML systems that drive measurable business impact, this role is for you WHAT YOU WILL DO 🏄 End-to-End ML Systems Ownership: Design, build, deploy, and monitor production ML models and pipelines—such as the IAB Segmentation Model, Retailer & Category Affinity Segments, and Custom Segment Toolkits. Feature & Data Engineering at Scale: Build and maintain high-volume feature engineering and data pipelines using Spark, Databricks, Python, and SQL, working with core tables containing tens of billions of rows. MLOps & Model Lifecycle Management: Implement robust MLOps practices, including model registries, offline/online evaluation, experiment tracking (MLflow or equivalent), and monitoring for data/model drift and quality. Audience Platform Integration: Collaborate on the platform side (Audience API, User Profile API, Kafka Topics, and activation integrations like DV360, Braze, and TTD) to ensure models seamlessly integrate into downstream production services. Experimentation & Model Tuning: Design and execute offline evaluations and A/B tests to validate modeling impact, optimize hyperparameters, and unlock new data signals (e.g., Store Trip Data). Privacy & Compliance: Plan and execute modeling and data pipeline initiatives in strict accordance with data regulatory laws (GDPR, CCPA, consent management). Cross-Functional Collaboration & AI Workflows: Partner with Product Analytics, Marketing Science, Data Engineers, and Software Engineers to drive revenue OKRs. Utilize AI-assisted development tools (e.g., Cursor, Claude Code) to maximize daily throughput. WHAT YOU WILL NEED 🪄 Strong Software Engineering Fundamentals: Comfortable owning and operating production backend/data systems end-to-end, including participating in the team's on-call rotation. End-to-End Production ML Track Record: Proven experience across the full ML lifecycle: feature engineering on Databricks/Spark, model training, evaluation, deployment, and monitoring in production. Genuine Modeling & Statistics

Skills:
Ability to build, tune, and evaluate models from scratch, with solid grounding in statistics, hyperparameter tuning, and experiment design (A/B testing). Data Scale & Tech Stack Mastery: Strong proficiency in Python, Spark, SQL, and Databricks. Comfort with distributed data pipelines at real scale. MLOps Fluency: Experience with ML lifecycle tools (MLflow or equivalent), model registries, and production monitoring for drift/quality. Active Use of AI Coding Agents: Hands-on use of AI development tools (e.g., Cursor, Claude Code) to increase personal engineering throughput.

Education:
Bachelor's degree in Computer Science, Mathematics, Physics, Engineering, or a related quantitative field. Fluent in English: Strong written and spoken communication skills with overlap availability into North American hours (9am to 12pm EST) 👉 At our company, we value diversity and actively encourage it — we believe a variety of perspectives and backgrounds makes us stronger. We focus on potential rather than on having a “perfect” CV. If this role excites you and you believe you could grow into it — even if you don’t tick every single box in the requirements — we’d love to hear from you NICE TO HAVE 🎆 Ad-tech or programmatic advertising context. Privacy engineering background (GDPR, CCPA). Deep Databricks platform administration knowledge. Familiarity with LLM platforms (OpenAI, Gemini) and embedding research. SALARY RANGE ⚖️ : €50,000 – €70,000 fixed gross salary per year. LOCATION 🌐 While we have offices in Milan, Barcelona and Sofia, you can benefit from our flexible hybrid model, empowering you to work where you’re most effective in Europe. Full remote available from Europe. WHAT YOU WILL FIND AT SHOPFULLY 🤗 🌎 An opportunity to thrive in a rapidly scaling multinational company 🕶️ A vibrant, informal, and inclusive work environment 🧠 We champion autonomy, flexibility, and a hybrid work model, empowering you to own your work 📚 Access to learning opportunities and regular feedback sessions 🍓 Enjoy our central, modern offices featuring fresh snacks, coffee (including vegan options), and ergonomic setups 🎉 Engage in meaningful team events: offsites, happy hours, company parties and celebrations that unite us beyond daily tasks 💻We provide all the necessary equipment for you to work effectively and set up your workspace, wherever you are 🧰 Benefit from additional country-specific advantages based on local contracts and practices WHO WE ARE 💜 We’re a team of 450 people (and counting), from 30 different nationalities. We’re on a mission to innovate, and we believe the key to that is seeing the world through a variety of perspectives. That’s why building a more diverse team, as well as bringing in even more talent, is so important for us. TO FEEL AT HOME 🛋️ IN SHOPFULLY YOU NEED 🎯 Progress Over Perfection: We move forward. Always. In a fast-moving world, speed with purpose beats certainty without action. Momentum matters—not for its own sake, but because it drives results. Clarity Through Transparency: We bring each other along. We work in teams, not silos. Transparency gives context, and context enables action. That means we share decisions early, publish work in progress, and document outcomes so others can learn, move faster, and build better. Learn Loudly: We grow by doing and by daring. Big bets unlock big breakthroughs. But real innovation demands risk, and risk always carries the possibility of failure. That’s not a flaw. It’s the price of ambition. Challenge with Empathy: We speak up, even when it’s hard—because candor shows care, and leads to better outcomes. Listening deeply is how we grow. Disagreeing openly is how we build trust. Always Build Better: We are builders—of products, systems, and ideas. But more than that, we are builders of better. Better has no finish line. It’s a mindset.
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