Post Masters Research Associate – Large Language Models

Pacific Northwest National Laboratory • UNAVAILABLE, UNAVAILABLE

1d

About The Position

The Optimization and Control Group within the Electricity Infrastructure and Buildings Division is seeking a Post-Masters Research Associate to contribute to research developing agentic systems that address interoperability challenges in buildings and related domains. The position applies artificial intelligence, particularly large language models (LLMs), to challenges that hinder the large-scale deployment of smart applications. The researcher will help design autonomous workflows that turn complex physical-system data into actionable, automated decisions.

The role involves working with PNNL staff to design, implement, and test agentic AI frameworks for residential and commercial buildings. The work requires understanding large language models, frontier AI, retrieval-augmented protocol, model context protocol, and multi-agent systems for large-scale deployments. The successful candidate will implement and test frameworks in Python, summarize technical findings in presentations and/or peer-reviewed publications, and collaborate with a multidisciplinary team to connect legacy infrastructure with next-generation control systems.

Candidates must have received a master’s degree within the past 24 months or expect to receive one within the next 8 months from an accredited college or university. The position is based at Pacific Northwest National Laboratory in eastern Washington. The posting states that Research Associates are excluded from the listed Rockstar Rewards benefits; all benefits are eligibility-dependent.

Requirements

• Must have received a master’s degree within the past 24 months or expect to receive one within the next 8 months from an accredited college or university.
• Understanding of large language models, frontier AI, retrieval-augmented protocol, and model context protocol.
• Experience developing multi-agent systems for large-scale deployments.
• Ability to implement and test frameworks in Python.

Nice To Haves

• Master’s degree in Computer Science, Data Science, Engineering, Applied Mathematics, or a closely related discipline.
• Proficiency in Python programming for complex data analysis, task automation, and algorithm development.
• Hands-on experience developing, evaluating, or deploying artificial intelligence and machine learning models, particularly generative AI and large language models.
• Strong understanding of agentic systems and reasoning frameworks.
• Prior experience related to building energy systems, smart grid technologies, or smart building controls.
• Track record of peer-reviewed publications or academic contributions in artificial intelligence or engineering applications.

Responsibilities

• Contribute to research developing agentic systems to address interoperability challenges in buildings and related domains.
• Design autonomous workflows that translate complex physical-system data into actionable, automated decisions.
• Work with PNNL staff to design, implement, and test agentic AI frameworks for residential and commercial buildings.
• Implement and test frameworks in Python.
• Help summarize technical findings in presentations and/or peer-reviewed publications.
• Collaborate with a multidisciplinary team to bridge legacy infrastructure and next-generation control systems.

Benefits

• The posting describes health insurance and flexible work schedules as benefits at PNNL, but states that Research Associates are excluded from the listed Rockstar Rewards benefits and that all benefits depend on eligibility.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level