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Sr Distinguished Applied Researcher (World Models)

LinkedIn Capital One San Francisco, CA
Not Applicable Posted April 17, 2026 2 variants Job link
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Requirements
  • You’re comfortable with open-source languages and are passionate about developing further.
  • You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
  • Has a deep understanding of the foundations of AI methodologies.
  • Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.
  • An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes.
  • Experience in delivering libraries, platform level code or solution level code to existing products.
  • A professional with a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects.
  • Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.
  • Worked on scaling graph models to greater than 50m nodes Experience with large scale deep learning based recommender systems
  • Experience with production real-time and streaming environments
Preferred Skills
  • PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields
  • Behavioral Models
  • PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)
  • Member of technical leadership for model deployment for a very large user behavior model
  • Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR
  • Worked on scaling graph models to greater than 50m nodes Experience with large scale deep learning based recommender systems
  • Experience with production real-time and streaming environments
  • Contributions to common open source frameworks (pytorch-geometric, DGL)
  • Proposed new methods for inference or representation learning on graphs or sequences
Education
  • (Not required) – PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 8 years of experience in Applied Research
  • (Not required) – PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields
  • (Not required) – PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)