A

Argonne National Laboratory

Advanced scientific research and computing facilities

Radiofrequency Engineer

Full-TimeUpdated
$162.8k - $253.9k
Expert
Bachelor's, Master's, PhD
Lemont, IL, USA
In Person
Electrical EngineeringRF & Microwave Engineering
Company Does Not Provide H1B Sponsorship

About the job

Requirements
  • A Bachelor's degree with 15 or more years of experience, a Master's degree with 12 or more years of experience, a PhD with 10 or more years of experience, or equivalent experience.
  • Experience in high-power radiofrequency system design, construction, operation, and maintenance, with emphasis on multi-kilowatt solid-state radiofrequency power amplifier technology.
  • Working knowledge of high-power radiofrequency technology at frequencies between 10 MHz and 1,000 MHz, and hands-on experience with laterally diffused metal-oxide-semiconductor solid-state radiofrequency power amplifiers.
  • Experience with high-power radiofrequency components, including waveguide transmission systems, ferrite circulators and isolators, radiofrequency power couplers, radiofrequency loads, radiofrequency power combiners and splitters, and directional couplers.
  • Operational knowledge of multi-kilowatt radiofrequency amplifier systems.
  • Knowledge of understanding complex systems and identifying and isolating problems.
  • Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.
  • Interpersonal skills, oral and written communication skills, and ability to interact with people at all levels within and outside the laboratory.
Responsibilities
  • Design, develop, construct, implement, and maintain high-power radiofrequency systems for particle accelerator applications using solid-state radiofrequency amplifier devices for power generation in the 10 MHz to 1 GHz frequency range.
  • Apply analytical methods and circuit simulation software tools to design and optimize solid-state radiofrequency power amplifier performance.
  • Design and implement interlock systems to protect high-power radiofrequency components and systems.
  • Analyze radiofrequency system performance to provide fault analysis and improve operational reliability.
  • Act as lead engineer in troubleshooting high-power radiofrequency systems and independently design and implement system improvements.
  • Plan and lead research and development programs related to high-power radiofrequency systems and devices.
  • Independently perform complex electrical and electronics engineering analysis and troubleshoot and debug radiofrequency systems.
  • Formulate and implement maintenance and operating guidelines for solid-state amplifier systems, and provide engineering support to the Operation and Analysis Group.
  • Perform other duties as assigned when required.
Desired Qualifications
  • Knowledge and experience with accelerating cavities and vacuum-tube radiofrequency technology.
  • Operating experience with alternating-current power distribution systems for high-power radiofrequency installations.

About the company

A

Argonne National Laboratory

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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

Growth & Insights and Company News

6 month growth

1%

1 year growth

1%

2 year growth

1%
The Register
Oct 7th, 2026
Argonne's AI X-ray nanoprobe beamline responds to natural language commands to analyse samples

Scientists at Argonne National Laboratory have developed an AI-enhanced X-ray microscope that researchers can control using natural language commands. The system combines an agentic AI platform with a hard X-ray nanoprobe beamline, allowing users to simply ask the microscope to examine specific areas of interest. The technology addresses a major challenge in X-ray imaging: modern experiments can generate millions of images in hours, requiring days of conventional analysis. Argonne's AI system reconstructs images in real time as data arrives. The microscope can autonomously collect data, process results, and identify additional areas requiring investigation. Computational scientist Mathew Cherukara said the AI-enhanced microscope could benefit numerous scientific fields beyond its initial microelectronics demonstration. The project forms part of the SYNAPS-I initiative, led by Lawrence Berkeley National Laboratory under the Department of Energy's Genesis Mission, launched in November 2025.

Associated Press
Jul 7th, 2026
Argonne's ChemGraph uses AI to automate computational chemistry workflows

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 Register
May 27th, 2026
Argonne National Laboratory launches private AI inference service using spare supercomputer capacity

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.

Business Wire
Mar 26th, 2026
Argonne Lab develops GridMind AI agent to support power grid operators

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.

Yahoo Finance
Mar 11th, 2026
AI adviser helps Argonne's robotic lab discover advanced electronic materials in just 64 experiments

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.