Senior ML Research Engineer – AI & Generative Recommendation (m/f/d)

None  •  IT & Software  •  Berlin, Germany

<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <strong>adjoe</strong> is redefining the future of mobile ads. Powered by advanced AI, first-party data and world-class engineers, we’ve perfected the offerwall experience for monetizing and scaling app publishers with solutions like Playtime – now the fastest growing rewarded advertising channel globally – driving incremental engagement, retention, and revenue. Together, this ecosystem connects app developers to over 770 million users worldwide for scalable growth. We are a profitable, high-growth company backed by a $100 million investment from Bertelsmann. Operating from offices in Hamburg, Boston, Singapore, and Tokyo, adjoe is defining the next stages of app and ad experience – right now. Join us.<br/><br/><strong>What You Will Do:<br/><br/></strong><ul><li>Architect Multi-Stage Recommendation Pipelines: Lead the design and implementation of our AI-Native Recommendation Pipelines, encompassing Generative Retrieval, Deep Ranking, and Re-ranking, serving millions of requests. </li><li>Bridge Theory to Production: Act as the team’s primary "Paper2Code Ninja," distilling SOTA research (NeurIPS, KDD, RecSys) into robust, production-ready code optimized for sub-millisecond latency. </li><li>Evolve Content Understanding: Direct the research on information-rich embeddings, utilizing unsupervised and generative approaches to create a unified representation of users and ads. </li><li>Scale Innovative Tech: Own the transfer of innovative technologies into high-concurrency environments, ensuring large-scale generative models thrive under extreme production constraints. </li><li>Act as a Team Multiplier: Lead architectural debates, mentor junior scientists, and foster a collaborative environment where constructive feedback pushes the team to excel. <br/><br/></li></ul><strong>Who You Are:<br/><br/></strong><ul><li>The Scholar-Architect: PhD in CS, Stats, Math, or Physics (or MSc with extensive industry research) and a track record of publications in top-tier venues (SIGIR, KDD, NeurIPS, ICLR, etc.). </li><li>RecSys Veteran: 4+ years of professional experience building large-scale recommenders in high-throughput industries (AdTech, E-com, or Social). </li><li>Full-Stack Researcher: Deeply proficient in PyTorch with experience in high-concurrency optimization, model quantization, and distillation. </li><li>Independent Driver: You don't need a roadmap; you create it. You thrive on solving "unsolved" problems with a direct impact on business ROI. <br/><br/></li></ul><strong>Fuel for the Journey: Benefits to Support Your Ambitions:<br/><br/></strong><ul><li>Invest in Your Future: Regular feedback and our development program support your growth, helping you expand your skillset and achieve your career goals. </li><li>Easy Arrival to adjoe: From signing to settling in Berlin, we’ve got you covered. Need a visa? No problem. Ready to build your new life and career at adjoe in Berlin? We support every ambition—from learning German to a relocation bonus that helps you settle in and make Hamburg feel like home. </li><li>Live Your Best Life, at Work and Beyond: We work in a hybrid setup with 3 core office days, plus flexible working hours. Enjoy 30 vacation days, 3 weeks of remote work per year, and free access to an in-house gym with lots of different fitness classes and mental health support through our Employee Assistance Program (EAP). </li><li>Join the Community! Participate in regular team and company events, including hackathons and social gatherings. We work together, and we celebrate together, too. <br/><br/></li></ul>We welcome applications from people who will contribute to the diversity of our company.<br/><br/> </div>

Job Overview
  • Datum der Veröffentlichung

    Jun 02, 2026

  • Kategorie

    IT & Software

  • Job Type

  • Standort

    Berlin, Germany

  • Arbeitgeber

    adjoe

  • Source

    LinkedIn