HPE Labs – AI/ML Research Scientist III

HPE•Milpitas, CA

3d•$136,500 - $276,500•Onsite

About The Position

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.

Requirements

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, with a high level of software proficiency.
Strong programming skills in Python, including proficiency with data structures and algorithms.

Nice To Haves

1-3 years of post-PhD research or industry experience preferred, but not required.
Experience in research and development involving LLMs, and agentic AI platforms.
Experience in research and development involving Reinforcement Learning and/or Digital Twins.
Deep knowledge of different deep learning model architectures, uncertainty quantification, optimization, and control.
Experience with generative models, including diffusion models and inverse models.
Experience with ML model optimization, GPU acceleration, heterogeneous computation, system software, and performance optimization.

Responsibilities

Conduct original research in areas including LLM reasoning and agents, reinforcement learning, agentic and multi-agent systems, generative diffusion models, inverse models, digital twins, trustworthy AI, tokenomics, optimization, and uncertainty quantification.
Conceive, design, and develop novel technologies addressing some of the most consequential challenges of our time, such as holistic data center co-optimization (computation, energy, water resources, power systems, waste heat recovery), nuclear fusion energy, AI-driven acceleration of AI workloads and quantum computing simulation on HPC systems, and trustworthiness of AI/LLM-based systems.
Develop novel methods for high-assurance multi-agent and multi-objective real-time control of complex cyber-physical systems, LLM-enabled explainable decision-making , agentic frameworks, and diffusion-model and inverse-model approaches for design and optimization.
Publish at leading venues (e.g., NeurIPS, ICML, ICLR, AAAI) and develop patent disclosures that strengthen HPE's intellectual property portfolio.
Bridge the gap between research and deployment by building robust software prototypes and engineering solutions that address use cases across domains such as data center and private cloud optimization, clean energy systems, and large-scale scientific computing.
Architect and implement systems involving GPU acceleration, heterogeneous computation, model optimization, and real-time data and streaming workflows to deliver AI capabilities at scale and under operational constraints.
Provide technical thought leadership within the core ML research team and across HPE by identifying, evaluating, and shaping emerging technology opportunities in AI and machine learning.
Contribute to HPE's research and product strategy by translating research insights into actionable technical roadmaps and by articulating the significance and potential of new directions to both technical and executive audiences.
Collaborate closely with internal research teams, engineering organizations, and business units across HPE to ensure research outcomes align with strategic priorities and create measurable value.
Engage with external partners, including academic institutions, national laboratories, and industry collaborators, to amplify research impact and maintain visibility within the broader research community.
Mentor junior researchers and contribute to the growth and development of the team's collective expertise.