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    <job>
      <externalid>817e09f4-2a1</externalid>
      <Title>Research Scientist, Recommendation Systems</Title>
      <Description><![CDATA[<p>As a Research Scientist, you will have the opportunity to build new paradigms using Large Language Models, harnessing the advanced content understanding, long-context, and reasoning capabilities.</p>
<p>You will play a pivotal role in exploring how to integrate data from recommendation domains into foundation models, enabling new capabilities through data curation, Supervised Fine-Tuning (SFT), Reinforcement Learning (RL) training, and more.</p>
<p>Key responsibilities:</p>
<ul>
<li>Research and develop key technologies such as Semantic IDs, generative retrieval/ranking, large user models.</li>
<li>Build prototypes to demonstrate the &#39;art of the possible&#39; for recommendation systems using the newest AI advances.</li>
<li>Work closely with product teams to translate research breakthroughs into deployed solutions for flagship products, tackling real-world challenges at an industrial scale through new recipes.</li>
</ul>
<p>We are seeking a Research Scientist who can drive new research ideas from conception and experimentation through to productionisation. In this rapidly shifting landscape, we regularly invent novel solutions to open-ended problems. You should be flexible, adaptable, and comfortable pivoting when ideas don&#39;t work out.</p>
<p>In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience:</p>
<ul>
<li>PhD in Machine Learning, Computer Science, or a relevant field (or equivalent practical research experience).</li>
<li>A proven track record of research excellence (e.g., publications at top-tier venues like NeurIPS, ICML, ICLR, or significant industry contributions), ranging from recent graduates to experienced researchers.</li>
<li>Strong software engineering skills to complement your research background.</li>
</ul>
<p>In addition, the following would be an advantage:</p>
<ul>
<li>Proven track record of building recommender / search systems and/or successfully deploying novel deep learning algorithms at industrial scale.</li>
<li>Skilled in LLM post-training algorithms and infra, with proficiency in JAX.</li>
<li>Strong communication skills with a demonstrated ability to drive cross-functional projects and collaborate effectively across organizational boundaries.</li>
</ul>
<p>At Google DeepMind, we want employees and their families to live happier and healthier lives, both in and out of work, and our benefits reflect that. Some select benefits we offer: enhanced maternity, paternity, adoption, and shared parental leave, private medical and dental insurance for yourself and any dependents, and flexible working options.</p>
<p style="margin-top:24px;font-size:13px;color:#666;">XML job scraping automation by <a href="https://yubhub.co">YubHub</a></p>]]></Description>
      <Jobtype>full-time</Jobtype>
      <Experiencelevel>senior</Experiencelevel>
      <Workarrangement>onsite</Workarrangement>
      <Salaryrange></Salaryrange>
      <Skills>Machine Learning, Computer Science, Semantic IDs, Generative Retrieval/Ranking, Large User Models, Supervised Fine-Tuning, Reinforcement Learning, JAX</Skills>
      <Category>Engineering</Category>
      <Industry>Technology</Industry>
      <Employername>Google DeepMind</Employername>
      <Employerlogo>https://logos.yubhub.co/deepmind.com.png</Employerlogo>
      <Employerdescription>Google DeepMind is a subsidiary of Alphabet Inc. that focuses on artificial intelligence research.</Employerdescription>
      <Employerwebsite>https://deepmind.com/</Employerwebsite>
      <Compensationcurrency></Compensationcurrency>
      <Compensationmin></Compensationmin>
      <Compensationmax></Compensationmax>
      <Applyto>https://job-boards.greenhouse.io/deepmind/jobs/7461811?utm_source=yubhub.co&amp;utm_medium=jobs_feed&amp;utm_campaign=apply</Applyto>
      <Location>Mountain View, California, US</Location>
      <Country></Country>
      <Postedate>2026-03-16</Postedate>
    </job>
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