Machine Learning Post Doctoral Research Scientist

Hewlett Packard Enterprise • Milpitas, CA

2d • $99,320 - $149,760 • 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 conducts research in areas including reinforcement learning for complex systems, fusion, large language models, agentic AI, generative diffusion models, trustworthy AI, optimization, and digital twins.

The role bridges frontier research and real-world applications, including nuclear fusion, supercomputing and data center sustainability, quantum computing acceleration, and trustworthy AI/LLM-based systems. The scientist will develop research and advanced agentic implementations involving generative diffusion models, machine-learning surrogates, agentic LLMs, and reinforcement learning, particularly as applied to nuclear fusion. Research areas also include LLM reasoning, multi-agent systems, generative modeling, digital twins, clean energy, and data center/private cloud optimization.

The work involves 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 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 venues such as NeurIPS, ICML, and AAAI, and developing patent disclosures. The role is hands-on and includes software prototyping and engineering, GPU acceleration, model optimization, and real-time data and streaming workflows for production-relevant AI capabilities.

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, including 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 is strongly preferred but not required.
• Experience with generative models is strongly preferred.
• 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 is desired.

Responsibilities

• Conduct original research and develop novel machine-learning technologies.
• Develop advanced agentic implementations involving generative diffusion models, machine-learning surrogates, agentic LLMs, and reinforcement learning, including applications to nuclear fusion.
• Research LLM reasoning, agentic and multi-agent systems, reinforcement learning, generative modeling, digital twins, clean energy, and data center/private cloud optimization.
• Work on real-time control of complex physical systems, explainable decision-making, cyber-physical systems, analytics and uncertainty quantification, and diffusion-model approaches to 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 such as NeurIPS, ICML, and AAAI, and develop patent disclosures.
• Build software prototypes and engineered solutions, including GPU acceleration, model optimization, and real-time data and streaming workflows.

Benefits

• Comprehensive benefits supporting physical, financial, and emotional wellbeing.
• Career development programs to support professional goals.

What This Job Offers

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