Argonne National Laboratory
Advanced scientific research and computing facilities
AI Systems Administration Specialist
Hybrid role; two or more onsite days per week in Lemont, Illinois.
About the job
- Experience in UNIX systems administration, especially Linux, with an emphasis on OS installation and upgrading, package building and management, common services and applications, and troubleshooting
- Experience with Salt, Ansible or similar configuration management tools
- Experience with Git, or other modern version control platforms
- Effective problem-solving skills
- Working knowledge of scripting languages, particularly Python
- Ability to write concise documentation
- Ability to work effectively as a member of a team
- Flexibility in handling assignments and working on several projects simultaneously
- Knowledge and understanding of how to safely operate within a datacenter, including tasks such as mounting and unmounting server hardware
- Ability to handle physical labor of installing racks and servers in a datacenter including lifting up to 20 pounds independently and upwards with additional help from others
- Understanding of IPv4 networking
- Ability to model Argonne’s core values: impact, safety, respect, integrity and teamwork
- To perform the essential functions of this position successful applicants must provide proof of U.S. citizenship, which is required to comply with federal regulations and contract.
- PT3: Bachelor's degree and 4+ years of experience, or a Master's degree and 2+ years of experience, or equivalent
- PT4: Bachelor's degree and 6+ years of experience, or a Master's degree and 4+ years of experience, or equivalent
- Work directly with first class systems alongside scientific staff and research colleagues within the division
- Serve as a systems administrator working on Argonne’s AI Testbed, you will work with teams to install and manage a diverse array of AI and machine learning related hardware and software
- Work directly with other subject matter experts to ensure the sustainability and availability of the testbed infrastructure
- Support machines in a mixed operating system environment and work efficiently with other operations groups
- Have researchers rely on your guidance when it comes to the environment so you will have a direct impact in keeping research productive
- Work in a hybrid environment with 2+ days onsite in Lemont, Illinois; with the ability to work fully onsite if preferred by the applicant.
- Knowledge of AI/ML systems architecture and workflows (Groq, Cerebras, Graphcore, SambaNova)
- Working knowledge of Kubernetes management
- Knowledge of scientific applications
- Knowledge of high-performance networking technologies such as Infiniband and Slingshot
- Knowledge of Storage Area Networking and storage arrays, such as NetApp.
- Knowledge of parallel and distributed file systems such as Lustre, and their associated hardware
- Knowledge of high-performance computing techniques, graphics, and visualization
- Experience with software packaging, building software from source, and dynamic linking
- Understanding of MPI, and implementations
- Ability to gather site requirements and represent them to design and development teams to find appropriate solutions across multiple sites
- Ability to independently assess requirements, identify tasks, and coordinate with peers to accomplish goals
- Experience implementing CI or CD workflows
About the company
Argonne National Laboratory advances scientific discovery and sustainability by providing access to large-scale research facilities and high-performance computing for government, academia, and industry partners. Researchers use the Advanced Photon Source for atomic-level materials studies and the Argonne Leadership Computing Facility for complex simulations and data analysis. The lab differentiates itself through its shared-use, multi-institution partnerships and a focus on eco-innovation, net-zero goals, and AI accelerator development. Its aim is to address real-world energy, materials, and data science challenges by combining cutting-edge infrastructure with collaborative research efforts.
Company Size
5,001-10,000
Company Stage
Grant
Total Funding
$19.7M
Headquarters
Lemont, Illinois
Founded
1946
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Simplify's Take
What believers are saying
- UChicago Argonne LLC won a renewed DOE contract in 2026 for five years.
- 2026 launch of ChemGraph, GridMind, and AI inference services deepens Argonne's relevance.
- Activated Materials Lab opened in 2026, expanding nuclear fuels research and user-facility demand.
What critics are saying
- DOE's FY2027 request cuts Argonne funding about $155.8 million, hitting programs and construction.
- 2025 buyouts showed staff pressure; more layoffs follow if Congress accepts budget cuts.
- A prolonged federal reset or shutdown threatens APS, Aurora utilization, and Argonne's operating model.
What makes Argonne National Laboratory unique
- Aurora and APS create a rare AI-plus-X-ray discovery stack for 2026 science.
- Chain Reaction Innovations embeds startups inside Argonne with facilities, mentors, and DOE funding.
- Q-NEXT and other DOE centers keep Argonne central to U.S. quantum research.
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Benefits
Health Insurance
Dental Insurance
Vision Insurance
Life Insurance
Disability Insurance
Paid Vacation
Paid Sick Leave
Paid Holidays
Remote Work Options
Flexible Work Hours
401(k) Retirement Plan
401(k) Company Match
Professional Development Budget
Wellness Program
Growth & Insights and Company News
6 month growth
↓ -2%1 year growth
↓ -2%2 year growth
↓ -2%Researchers at the US Department of Energy's Argonne National Laboratory have developed ChemGraph, an open-source framework that uses artificial intelligence to automate computational chemistry workflows. The tool provides a natural language interface, allowing researchers to describe scientific problems in plain language, which the system then maps onto computational tasks and analyses. ChemGraph was developed using resources at the Argonne Leadership Computing Facility, including the Aurora exascale supercomputer. The framework uses AI agents to handle different workflow tasks, from planning to execution and data aggregation. It calls appropriate scientific tools to reduce the risk of AI hallucination, using AI to run physics-based simulations rather than relying solely on existing knowledge. The framework supports applications in combustion efficiency, critical materials, and next-generation batteries, and complements DOE's Genesis Mission to accelerate science through AI.
The US Department of Energy's Argonne National Laboratory has launched an AI inference service using spare supercomputing capacity to support researchers across DoE labs and the Genesis Mission. The service runs on two clusters: Sophia, with 192 Nvidia A100 GPUs, and Metis, featuring 32 SambaNova SN40L AI accelerators. The platform provides secure access to various large language models, including OpenAI's GPT-OSS, Google's Gemma, and Meta's Llama, through a chatbot-like portal. Researchers are using the service to analyse experimental data in real time, including predicting plasma disruptions in fusion energy research and processing data from particle accelerators and telescopes. Argonne plans to extend the service to its Nvidia GH200-based Tara and B200-based Minerva systems, enabling scientists to experiment with AI without building their own infrastructure.
Researchers at the US Department of Energy's Argonne National Laboratory have developed GridMind, an agentic AI system designed to assist power grid operators through natural language interaction. The system functions as a reasoning co-pilot for control rooms, simplifying complex grid management tasks. GridMind employs a multi-agent architecture where specialised AI agents handle different functions, such as power scheduling and weather-based contingency planning. Large language models coordinate these agents to analyse situations, reason across different tasks and provide explainable recommendations. The system transforms technical analysis into conversational support whilst maintaining rigorous accuracy. Tests on standard power grid models demonstrated that GridMind consistently produced correct results across multiple state-of-the-art language models. The technology aims to accelerate decision-making by integrating disconnected workflows into a coherent reasoning engine.
A research team led by the US Department of Energy's Argonne National Laboratory has developed an AI adviser that optimises machine learning algorithms during autonomous experiments, accelerating discovery of advanced electronic materials. The system was applied to Polybot, Argonne's AI-guided robotic laboratory, to investigate mixed ion-electron conducting polymers for wearable electronics and energy storage. The adviser evaluates algorithm performance in real time and communicates insights to scientists who refine experimental plans. It reduced the study to just 64 experiments from over 4,300 possible combinations. During testing, the adviser suggested switching AI algorithms, leading to significant performance improvements, and identified deposition speed as a key performance driver. The research was published in Nature Chemical Engineering and included collaborators from the University of Chicago, Lawrence Berkeley National Laboratory and other institutions.
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