HPE Labs - Senior AI Researcher, Foundational AI

Hewlett Packard Enterprise•Milpitas, CA

1d•$152,000 - $349,000•Hybrid

About The Position

HPE Labs is seeking a Senior AI Researcher for the HPE Quantum team within Emergent Machine Intelligence. This is 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.

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 control, testing, debugging, profiling, performance optimization, and reproducible experimentation.
Strong knowledge of state-of-the-art AI/ML algorithms and the engineering skills to adapt, implement, and apply them to real-world problems when needed.
Strong written and verbal communication skills, with the ability to explain complex technical ideas, present research findings, and write high-quality scientific papers.
Demonstrated ability to work autonomously and collaborate effectively in an interdisciplinary research environment.

Nice To Haves

Substantial research background in theoretical physics, statistical physics, quantum physics, condensed-matter physics, or another mathematically intensive area of physics, together with a proven research record in AI and machine learning.
Research experience combining machine learning with physics, applied mathematics, dynamical systems, optimization, or scientific computing.
Experience moving fluidly between mathematical formulation, algorithm development, implementation, and empirical validation.
Working proficiency in C++.
Record of releasing high-quality open-source research software, models, benchmarks, datasets, or evaluation frameworks.
Experience developing reusable experimental infrastructure or tools that accelerate research across multiple projects.
Familiarity with neural quantum states, quantum many-body systems, quantum simulation, scientific machine learning, or other applications of AI to quantum science.
Experience with advanced computational physics or chemistry methods, such as Monte Carlo simulations, density functional theory (DFT), molecular dynamics (MD), or related numerical methods.

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.