Machine Learning Post Doctoral Research Scientist

Hewlett Packard Enterprise • Milpitas, CA

17h • Onsite

About The Position

This Post Doctoral Research Scientist role is part of Hewlett Packard Labs’ core Machine Learning research team in the San Francisco Bay Area. The team’s research spans reinforcement learning for complex systems, fusion, large language models, agentic AI, generative diffusion models, trustworthy AI, optimization, and digital twins. The role connects frontier research to real-world applications in areas including nuclear fusion, supercomputing and data center sustainability, quantum computing acceleration, and the trustworthiness of AI- and LLM-based systems.

The position focuses on developing research and technologies involving generative diffusion models, ML surrogates, agentic LLMs, and reinforcement learning, particularly for nuclear fusion applications. Research may cover LLM reasoning, agentic and multi-agent systems, reinforcement learning, generative modeling, digital twins, clean energy, and data center or private cloud optimization. Example topics include real-time multi-agent and multi-objective control of complex physical systems, LLM-enabled explainable decision-making, agentic frameworks for cyber-physical systems, analytics and uncertainty quantification, and diffusion-model approaches to design and optimization.

The scientist will provide technical thought leadership, collaborate with internal teams and external partners, and contribute to HPE’s research and product strategy by identifying emerging technology opportunities. Success includes publishing at leading research venues such as NeurIPS, ICML, and AAAI, and developing patent disclosures. The work is hands-on and includes software prototyping and engineering, GPU acceleration, model optimization, and real-time data and streaming workflows for production-relevant applications.

Requirements

• PhD in Computer Science, Electrical Engineering, or a related field, with a dissertation focused on Machine Learning.
• Extensive experience with emerging machine learning techniques, such as diffusion models, LLMs, multi-agent systems, and reinforcement learning.
• Experience developing applications with deep learning frameworks such as PyTorch.
• High level of software proficiency.
• Strong programming skills in Python, data structures, and algorithms.

Nice To Haves

• Experience applying machine learning to nuclear fusion.
• Experience with generative models.
• Deep knowledge of deep learning model architectures, uncertainty quantification, optimization, control, digital twins, and world models.
• Experience with ML model optimization, GPU acceleration, heterogeneous computation, system software, and performance optimization.

Responsibilities

• Conduct original research and develop novel technologies in machine learning and related areas.
• Develop research involving generative diffusion models, ML surrogates, agentic LLMs, and reinforcement learning, including applications to nuclear fusion.
• Research LLM reasoning, agentic and multi-agent systems, generative modeling, digital twins, clean energy, and data center or private cloud optimization.
• Work on multi-agent and multi-objective real-time control of complex physical systems, explainable decision-making, cyber-physical system agentic frameworks, analytics, uncertainty quantification, and diffusion-model design and optimization.
• Provide technical thought leadership and collaborate with internal teams and external partners.
• Contribute to HPE’s research and product strategy by identifying and shaping emerging technology opportunities.
• Publish research at leading venues and develop patent disclosures.
• Build software prototypes and engineered AI capabilities, including GPU acceleration, model optimization, and real-time data and streaming workflows.

Benefits

• A comprehensive suite of benefits supporting physical, financial, and emotional wellbeing.
• Career development programs to support professional goals and skill development.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree