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You will work on improving Gemini&#39;s quality of information-seeking responses, focusing on helpfulness, factuality, grounding, and other aspects. This is an opportunity to explore fundamental issues in modeling and data interventions for information-seeking scenarios, with significant opportunities in shaping Google&#39;s products in this space.</p>\n<p>Responsibilities:\nResearch on post-training (e.g., RL and SFT) for information-seeking scenarios in Gemini\nResearch on novel evaluation methods for improving model quality, grounding, and factuality\nResearch on orchestration of tool calls, and improved retrieval methods, for information-seeking scenarios</p>\n<p>About You:\nTo succeed in this role, you should have a PhD in a relevant area, or an equivalent research/publication record. You should also have strong software-engineering skills in addition to a research background. 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You should be flexible, adaptable, and comfortable pivoting when ideas don&#39;t work out.</p>\n<p>To succeed in this role, you will need:</p>\n<ul>\n<li>A PhD in Machine Learning, Computer Science, or a relevant field (or equivalent practical research experience).</li>\n<li>A proven track record of research excellence (e.g., publications at top-tier venues like NeurIPS, ICML, ICLR, or significant industry contributions).</li>\n<li>Strong software engineering skills to complement your research background.</li>\n</ul>\n<p>In addition, hands-on experience with modern post-training methods (SFT, RLHF, etc.) and prior work applying LLMs to personalisation, memory, or agentic workflows would be an advantage.</p>\n<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 include enhanced maternity, paternity, adoption, and shared parental leave, private medical and dental insurance for yourself and any dependents, and flexible working options.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_ccdfdbc6-9b5","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7477025","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$141,000 USD - 244,000 USD + bonus + equity + benefits","x-skills-required":["Machine Learning","Computer Science","Software Engineering","Post-training techniques","RL","SFT","Preference optimisation","Evaluation frameworks","Simulation methods","Model quality","User behaviours","Feedback","Agent design","Tool orchestration","API integration","Hyper-personalisation"],"x-skills-preferred":["Modern post-training methods","LLMs","Personalisation","Memory","Agentic workflows"],"datePosted":"2026-03-16T14:40:12.333Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View, California, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Computer Science, Software Engineering, Post-training techniques, RL, SFT, Preference optimisation, Evaluation frameworks, Simulation methods, Model quality, User behaviours, Feedback, Agent design, Tool orchestration, API integration, Hyper-personalisation, Modern post-training methods, LLMs, Personalisation, Memory, Agentic workflows","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":141000,"maxValue":244000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d0214534-b6a"},"title":"Senior Applied Scientist","description":"<p>We&#39;re building the next-generation Grounding Service that powers the latest AI applications—chat assistants, copilots, and autonomous agents—with factual, cited, and trustworthy responses. 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Builds citation and provenance systems (e.g., passage highlighting, quote-level alignment, confidence scoring) to reduce hallucinations and increase user trust. Leads experimentation and evaluation using A/B testing, interleaving, NDCG, MRR, precision/recall, and calibration curves to guide measurable trade-offs. Advances tool-augmented grounding through schema-aware retrieval, function calling, knowledge graph joins, and real-time connectors to databases, cloud object stores, search indexes, and the web. Partners with platform engineering to productionize models with scalable inference, embedding services, feature stores, caching, and privacy-compliant multi-tenant systems. Nurtures collaborative relationships with product and business leaders across Microsoft, influencing strategic decisions and driving business impact through technology. Authors white papers, contributes to internal tools and services, and may publish research to generate intellectual property. 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