Drimva

HPE

HPE Labs – AI/ML Research Scientist III

$136K–$276K

Milpitas, California, United States of America · Full-time · Posted Jul 13, 2026

Experience
Extensive research experience with Large Language Models and Reinforcement Learning; 1-3 years of post-PhD research or industry experience preferred, but not required.
Education
PhD in Computer Science, Electrical Engineering, or a related field, with a dissertation focus on Machine Learning.

About the job

Conduct frontier AI/ML research and build prototypes for applications including data center optimization, clean energy, scientific computing, and AI systems, while contributing to HPE’s research strategy.

What you'll do

  • Conduct original research, develop novel methods, and publish research or prepare patent disclosures.
  • Build software prototypes and systems for applied AI, including GPU-accelerated and real-time workloads.
  • Shape technical strategy, collaborate with internal and external partners, and mentor junior researchers.

What they ask for

  • PhD in Computer Science, Electrical Engineering, or a related field, with a dissertation focus on Machine Learning.
  • Extensive research experience with Large Language Models and Reinforcement Learning.
  • Experience developing applications with deep learning frameworks such as PyTorch.
  • Strong programming skills in Python, including proficiency with data structures and algorithms.

Nice to have

  • 1-3 years of post-PhD research or industry experience preferred, but not required.
  • Experience involving LLMs and agentic AI platforms, Reinforcement Learning and/or Digital Twins, or related model optimization and control areas.

Pay

USD 136,500 - 276,500 (base salary)

Tools

PyTorch, Python, GPU acceleration, heterogeneous computation

AI focus

Research and develop LLM reasoning and agents, reinforcement learning, agentic and multi-agent systems, generative and inverse models, digital twins, trustworthy AI, optimization, and uncertainty quantification; apply AI to complex systems and real-world workloads.

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First seen Oct 15, 2026 · Last verified Nov 6, 2026 (UTC)