Senior Research Scientist, Specialized Professional Intelligence

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* Software Engineering

Mountain View, CA (HQ)

** About X:*
* X is Alphabet's moonshot factory with a mission of inventing and launching "moonshot" technologies that could someday make the world a radically better place. We are a diverse group of inventors and entrepreneurs who build and launch technologies that aim to improve the lives of millions, even billions, of people. Our goal: 10x impact on the world's most intractable problems, not just 10% improvement.

We approach projects that have the aspiration and riskiness of research with the speed and ambition of a startup. As an innovation engine, X focuses on repeatedly turning breakthrough-technology ideas into the foundations for large, sustainable businesses.

** About the team:*
* We're a small, passionate and driven team of experienced ML researchers, software engineers and product leaders creating new AI capabilities for specialized professionals. Our moonshot is to radically transform how highly skilled professionals like lawyers, medical professionals or finance experts interact with information so they can harness AI's productivity gains. As we scale support for our early customers in the legal space, we're looking for people who are inspired to push the boundaries of what's technically possible and shape a product from the ground up.

Our culture is one of mutual care and respect, individual excellence, teamwork and fun!

Learn more about our moonshot here:

(Use the "Apply for this Job" box below)./

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* About the role:

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* As a Senior Research Scientist you will be a key architect of the core AI systems powering our professional intelligence platform. You will focus on building next-generation agentic systems designed to perform complex, deep reasoning and high-fidelity inference over extremely large corpora of unstructured data.

We look for engineers who bridge the gap between applied research and production. In this role, you will design, experiment with, and scale multi-component AI systems for highly specialized domains. This requires a strong foundational understanding of machine learning principles combined with solid software engineering experience to deploy robust architectures into production.

** What you should have:*
* + MSc or PhD in Computer Science, Mathematics, Machine Learning, or a related quantitative field

+ 6+ years of experience building, scaling, and product ionizing complex machine learning systems, with a strong focus on processing unstructured text data at scale

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Experience with information retrieval techniques (classical and embedding-based), knowledge graphs, or named entity recognition/reconciliation over large datasets

+ Experience building inference systems that use LLMs as building blocks, such as LLM-as-a-judge evaluation pipelines or ensemble-based estimators

+ Experience building custom agent harnesses, mastering the nuances of context engineering, state management, and optimization for multi-step reasoning loops

+ Strong proficiency in Python and modern software engineering best practices (architecture design, rigorous testing, CI/CD, and version control)

+ Experience implementing robust evaluation frameworks and performance monitoring for complex, non-deterministic AI systems in production

+ Demonstrated ability to lead multi-quarter technical projects from concept to deployment, including guiding and mentoring junior team members

+ Excellent written and verbal communication skills (e.g., design docs, technical specs) with an ability to translate ambiguous product/domain problems into concrete engineering roadmaps

+ Ability to work in the Mountain View office at least 3 days per week to interface with the rest of the team

** It'd be great if you had these:*
* + Deep interest in the potential transformative effects of advanced AI systems and commitment to ensuring their safe development

+ Startup experience or a proven ability to iterate quickly, navigate high ambiguity, and parallelize experiments to optimize system performance

+ Experience training, fine-tuning, or distilling models for specialized task performance or latency optimization

+ Experience tailoring AI systems or complex software architectures to specific, highly regulated, or knowledge-intensive industries

+ A track record of public technical contributions, whether through open-source artifacts, robust AI tools, or peer-reviewed publications at top-tier machine learning venues (e.g., NeurIPS, ICML, ICLR, ACL)

+ A strong product sense and a passion for analyzing user behavior and feedback to proactively improve system accuracy

+ A consistent history…