# Principal AI Research Scientist (HPE Labs) at Hewlett Packard Enterprise

Hewlett Packard Enterprise is hiring a Principal AI Research Scientist (HPE Labs). Level rates it Builds AI ●●●●; you can [apply on Hewlett Packard Enterprise](https://jobsbylevel.com/go/ec34d589-6960-4b5b-9216-444cef62cb13?utm_medium=ai-agent).

AI Level 4, AI centrality 100 out of 100. Remote (Milpitas, California, United States of America).

## Details

- Company: [Hewlett Packard Enterprise](https://jobsbylevel.com/companies/hewlett-packard-enterprise)
- AI level: AI Level 4 (score 100 out of 100)
- Location: Remote (Milpitas, California, United States of America)
- Posted: August 16, 2026
- Apply: https://jobsbylevel.com/go/ec34d589-6960-4b5b-9216-444cef62cb13?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=principal-ai-research-scientist-hpe-labs-at-hewlett-packard-enterprise-981ce9

## Prepare for this job

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## Description

Principal AI Research Scientist (HPE Labs) This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office. Who We Are: Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. Job Description: HPE Labs is seeking a Principal AI Research Scientist for the HPE Quantum team within Emergent Machine Intelligence, a senior individual-contributor role focused on original, publication-driven research in areas including foundation model reasoning, representation learning, physics-inspired machine learning, mechanistic interpretability, and interdisciplinary work at the intersection of AI, quantum computing, and physical science. The ideal candidate is intellectually broad, experimentally strong, and highly autonomous, with strong research judgment, a track record of publishing at leading AI venues, and the ability to identify impactful problems, develop original ideas collaboratively, and mentor junior researchers. . Key Responsibilities: Independently lead research projects from initial brainstorming and problem formulation through mathematical development, implementation, experimentation, analysis, and publication. Translate early-stage ideas into concrete hypotheses and design rapid, decisive experiments to determine whether a direction should be expanded, revised, or discontinued. Produce original research suitable for publication at leading venues such as NeurIPS, ICML, ICLR, and comparable conferences and journals. Maintain broad and current knowledge of modern AI research and use that knowledge to identify emerging opportunities, relevant prior work, and meaningful open problems. Collaborate with researchers, engineers, interns, and external partners across AI, physics, quantum computing, and advanced computing systems. Develop and release reusable research assets, including open-source software, models, experimental infrastructure, benchmarks, or datasets, when appropriate. Requirements: PhD in Computer Science, Artificial Intelligence, Machine Learning, Physics, Mathematics, or other related fields. Typically, 5+ years' experience Post PhD graduate studies. Strong and sustained record of original research in modern artificial intelligence or machine learning. Or equivalent. Demonstrated ability to convert research ideas into rigorous, complete, and publishable outcomes, with a strong publication record at leading machine-learning venues such as NeurIPS, ICML, and ICLR, or comparable peer-reviewed venues. Broad and current command of the AI and machine-learning literature, extending beyond a single model family, technique, or application area. Deep mathematical understanding of modern machine-learning methods, including their objectives, assumptions, optimization behavior, learning dynamics, and limitations. Excellent experimental skills, including hypothesis formulation, rapid prototyping, controlled evaluation, analysis of failure modes, and interpretation of results. Advanced proficiency in Python and PyTorch, with experience using common AI/ML packages, libraries, and research tooling. Experience working with research codebases, open-source libraries, HPC and distributed systems, and collaborative software-development practices. Strong software-engineering and algorithm implementation skills for research, including code design, version

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