Machine Learning and LLM Research Scientist

Overview

Machine Learning and LLM Research Scientist

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

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 bridge frontier research with real-world impact, tackle challenges such as supercomputing and data center sustainability, nuclear fusion, and the trustworthiness of AI/LLM-based systems. 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 an office-based role and the selected candidate will be working from an HPE office in Spring, TX.

As part of the team, you will provide technical thought leadership, collaborate with internal teams and external partners, and contribute to HPE s research and product strategy by identifying and shaping emerging technology opportunities. Success in this role includes publishing at leading venues (e.g., NeurIPS, ICML, AAAI) and developing patent disclosures.

• Conduct original research and develop novel technologies across areas including LLM reasoning, agentic and multi-agent systems, reinforcement learning, generative modeling, digital twins, clean energy, and data center/private cloud optimization.
• Prototype software, optimize models, enable GPU acceleration, and design real-time data/streaming workflows to support production-relevant use cases.
• Provide technical thought leadership and collaborate with internal teams and external partners; shape emerging technology opportunities and contribute to research and product strategy.
• Publish results at leading venues and contribute to patent disclosures.

• PhD in Computer Science, Electrical Engineering, or related fields, focusing on Machine Learning for the dissertation.
• Extensive experience with Large Language Models, Agentic AI, 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 the refinements of Large Language Models and Agentic Framework.
• 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, Security-First Mindset

Health & Wellbeing

We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.

Personal & Professional Development

We also invest in your career because the better you are, the better we all are. We have specific programs…