Machine Learning and Generative AI Research Scientist

Overview

This role has been designed as onsite with an expectation that you will primarily work from an HPE office.

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.

This Research Scientist role sits within the core Machine Learning research team at Hewlett Packard Labs, HPE’s primary research organization, based in the San Francisco Bay Area. The team is recognized for award-winning, top-tier publications and foundational contributions spanning reinforcement learning for complex systems, large language models (LLMs), agentic AI, generative diffusion models, trustworthy AI, optimization, and digital twins.

As part of this team, you will bridge frontier research with real-world impact, tackling challenges such as supercomputing and data center sustainability, nuclear fusion, and the trustworthiness of AI/LLM-based systems. You will conduct original research and develop novel technologies across areas, including LLM reasoning, agentic and multi-agent systems, reinforcement learning, generative diffusion modeling, digital twins, clean energy, and data center/private cloud optimization.

The work includes multi-agent and multi-objective real-time control of complex physical systems, LLM-enabled explainable decision-making, agentic frameworks for cyber-physical systems, analytics and uncertainty quantification, and diffusion-model approaches for design and optimization.

You will be expected to provide technical thought leadership, collaborate closely with internal teams and external partners, and contribute to funding opportunities and to HPE’s research and product strategy by identifying and shaping emerging technologies. Success in this role also includes publishing at leading venues (e.g., NeurIPS, ICML, AAAI), and developing patent disclosures. The position is highly hands-on, involving software prototyping and engineering, GPU acceleration, model optimization, and real-time data/streaming workflows to deliver robust AI capabilities for production-relevant use cases.

This is a rare opportunity to operate at the intersection of AI research and high-impact applications with world-class collaborators.

• PhD in Computer Science, Electrical Engineering, or related fields, focusing on Machine Learning for the dissertation.
• Extensive experience with Large Language Models, Generative Models, and Reinforcement Learning.
• Experience in developing applications with deep learning frameworks like PyTorch, with a high level of software proficiency.

• 1-3 years of experience preferred, but not required.
• Experience in research and development in Large Language Models, Agentic frameworks, and Generative models.
• Experience in research and development in Reinforcement Learning and/or Digital Twins.
• Experience and deep knowledge of different deep learning model architectures, uncertainty quantification, optimization, and control.
• Strong programming skills in Python, data structures, and algorithms are required.
• Experience with Generative models is desired.
• Experience with ML model optimization, GPU acceleration, heterogeneous computation, system software, and performance optimization is desired.
• Experience in Python Web Frameworks – Django, Flask - a plus but not required.

Artificial Intelligence Technologies, Cross Domain Knowledge, Data Engineering, Data Science, Design Thinking, Development Fundamentals, Full Stack Development, IT Performance, Machine Learning Operations, Scalability Testing,…