We'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. Our platform stitches together retrieval, reasoning, and real-time data so that large language models stay anchored to enterprise knowledge, the public web, and proprietary tools. We're looking for a Senior Applied Scientist to lead end-to-end science for grounding: inventing retrieval and attribution methods, defining factuality/faithfulness metrics, and shipping production models and APIs that scale to billions of queries. You'll partner closely with engineering, product, research, and customers to deliver fast, reliable, and explainable answers with source citations across a diverse set of domains and modalities. As a team, we value curiosity, pragmatic rigor, and inclusive collaboration. We believe great systems emerge when scientists and engineers co-design metrics, models, and infrastructure—and when we obsess over customer impact, privacy, and safety. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction. Responsibilities
\nOwns the science roadmap for grounding—including retrieval, re-ranking, attribution, and reasoning—driving initiatives from problem framing to production impact. Designs and evolves state-of-the-art retrieval and RAG orchestration across documents, tables, code, and images. 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. Bridges the gap between researchers (e.g., Microsoft Research) and development teams, applying long-term research to solve immediate product needs. Leads high-stakes negotiations to ensure cutting-edge technologies are applied practically and effectively. Identifies and solves significant business problems using novel, scalable, and data-driven solutions. Shapes the direction of Microsoft and the broader industry through pioneering product and tooling work. Mentors applied scientists and data scientists, establishing best practices in experimentation, error analysis, and incident review. Collaborates cross-functionally with PMs, research, infrastructure, and security teams to align on milestones, SLAs, and safety protocols. Communicates clearly through design documentation, progress updates, and presentations to executives and customers. Contributes to ethics and privacy policies, identifies bias in product development, and proposes mitigation strategies.
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_d0214534-b6a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/senior-applied-scientist-37/","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Statistics","Econometrics","Computer Science","Electrical or Computer Engineering","Machine Learning","Information Retrieval","Large Language Model Development","Pretraining","Supervised Fine-Tuning","Reinforcement Learning","Optimizing LLM Inference"],"x-skills-preferred":["Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field","6+ years related experience (e.g., statistics, predictive analytics, research)","Demonstrated expertise in information retrieval, with publications in top-tier conferences or journals such as NeurIPS, ICML, ICLR, SIGIR, or ACL","Hands-on experience in large language model (LLM) development, including pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL)","Proven track record in optimizing LLM inference, or active contributions to open-source frameworks like vLLM, SGLang, or related projects"],"datePosted":"2026-03-08T22:16:41.766Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Beijing"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, Machine Learning, Information Retrieval, Large Language Model Development, Pretraining, Supervised Fine-Tuning, Reinforcement Learning, Optimizing LLM Inference, Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field, 6+ years related experience (e.g., statistics, predictive analytics, research), Demonstrated expertise in information retrieval, with publications in top-tier conferences or journals such as NeurIPS, ICML, ICLR, SIGIR, or ACL, Hands-on experience in large language model (LLM) development, including pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL), Proven track record in optimizing LLM inference, or active contributions to open-source frameworks like vLLM, SGLang, or related projects"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_5276e91e-221"},"title":"Senior Machine Learning Engineer, Recommendation Systems - PhD Early Career","description":"[2026] Senior Machine Learning Engineer, Recommendation Systems - PhD Early Career
\nSan Mateo, CA, United States
\nEarly Career
\nID: 5471
\nEvery day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.
\nAt Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.
\nWe’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.
\nA career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.
\nRecommendation Systems are a key growth lever at Roblox, driving retention, engagement, and monetization for hundreds of millions of users. This role offers the unique opportunity to redefine how users search and discover everything from the most interesting immersive experiences and digital avatars in our Marketplace to personalized advertising. You will solve a diverse range of high-scale ranking, retrieval, and personalization problems across our platform.
\nWe combine cutting-edge research —including deep learning, generative AI, and reinforcement learning techniques— with large-scale engineering to bridge experimentation and production; you'll design algorithms that operate at massive scale and shape the next generation of recommender systems for user-generated content.
\nTeams Hiring for This Role
\nYou Will
\nYou Have
\nYou may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.
\nAs you apply, you can find more information about our process by signing up for Speak\\_. You'll gain access to our practice assessment, comprehensive guides, FAQs, and modules designed to help you ace the hiring process.
\nFor roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page.
\nAnnual Salary Range
\n$195,780—$242,100 USD
\nRoles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_5276e91e-221","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Roblox","sameAs":"https://careers.roblox.com","logo":"https://logos.yubhub.co/careers.roblox.com.png"},"x-apply-url":"https://careers.roblox.com/jobs/7350081","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$195,780—$242,100 USD","x-skills-required":["recommender systems","search systems","information retrieval","generative models","deep learning","generative AI","reinforcement learning","multimodal models","LLMs","VLMs","VLAs","Python","C++","Go","Java"],"x-skills-preferred":["sparse/linear attention","top-tier, peer-reviewed venues","SIGIR","KDD","RecSys","ICLR","ICML","NeurIPS"],"datePosted":"2026-03-06T14:17:16.772Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Mateo, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"recommender systems, search systems, information retrieval, generative models, deep learning, generative AI, reinforcement learning, multimodal models, LLMs, VLMs, VLAs, Python, C++, Go, Java, sparse/linear attention, top-tier, peer-reviewed venues, SIGIR, KDD, RecSys, ICLR, ICML, NeurIPS","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":195780,"maxValue":242100,"unitText":"YEAR"}}}]}