adjoe - Senior ML Research Engineer – AI & Generative Recommendation (m/f/d)
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Requirements
• 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.). • The Scholar-Architect: • RecSys Veteran: 4+ years of professional experience building large-scale recommenders in high-throughput industries (AdTech, E-com, or Social). • RecSys Veteran: • Full-Stack Researcher: Deeply proficient in PyTorch with experience in high-concurrency optimization, model quantization, and distillation. • Full-Stack Researcher: • Independent Driver: You don't need a roadmap; you create it. You thrive on solving "unsolved" problems with a direct impact on business ROI. • Independent Driver: • Fuel for the Journey: Benefits to Support Your Ambitions: • Invest in Your Future: Regular feedback and our development program support your growth, helping you expand your skillset and achieve your career goals. • Invest in Your Future: • 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. • Easy Arrival to adjoe: • 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). • Live Your Best Life, at Work and Beyond: • We welcome applications from people who will contribute to the diversity of our company.
Responsibilities
• 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. • Architect Multi-Stage Recommendation Pipelines: • 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. • Bridge Theory to Production: • Evolve Content Understanding: Direct the research on information-rich embeddings, utilizing unsupervised and generative approaches to create a unified representation of users and ads. • Evolve Content Understanding: • Scale Innovative Tech: Own the transfer of innovative technologies into high-concurrency environments, ensuring large-scale generative models thrive under extreme production constraints. • Scale Innovative Tech: • Act as a Team Multiplier: Lead architectural debates, mentor junior scientists, and foster a collaborative environment where constructive feedback pushes the team to excel. • Act as a Team Multiplier:
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