Full-Time

HPC Network Engineer

Updated on 7/21/2026

Argonne National Laboratory

Argonne National Laboratory

1,001-5,000 employees

Advanced scientific research and computing facilities

Compensation Overview

$69.8k - $108.8k/yr

No H1B Sponsorship

Woodridge, IL, USA

Hybrid

Hybrid schedule with some onsite days.

Category
DevOps & Infrastructure (1)
Required Skills
TCP/IP

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Requirements
  • Strong knowledge of and experience with Ethernet based networks, designs, protocols, and services
  • Knowledge of media types such as Cat6a, SMF, MMF (OM4/OM5), transceiver types, and link negotiation
  • Knowledge of the Data Link Layer, especially MAC/CAM tables, 802.1Q tagging, ARP, and access lists
  • Knowledge of LACP/LAGs, MLAGs, STP, and path MTU
  • Knowledge of network-based services like DHCP
  • Experience with InfiniBand fabrics, Subnet Managers, and the use of the UFM
  • Familiarity with SAN and PFS storage concepts and design
  • Familiarity with version control systems for managing configurations
  • Problem-solving skills for troubleshooting issues
  • Experience working in collaborative teams, including refining user requirements and supporting researchers in scientific computing settings
  • Ability to model Argonne's core values of 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
Responsibilities
  • Under supervision, perform routine configuration of VLANs, interface speeds, and Link Aggregation Groups using the CLI
  • Assist in the racking, stacking, initial provisioning, and replacement of networking hardware
  • Responsible for the installation and labeling of networks using DAC, AOC, and fiber optics
  • Utilize monitoring tools to identify port errors, packet loss, or flapping links
  • Utilize the IB UFM or other tooling to verify fabric health and health-check HCAs
  • Use of configuration management workflows to track changes
  • Maintain accurate network documentation
Argonne National Laboratory

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

1,001-5,000

Company Stage

Grant

Total Funding

$19.7M

Headquarters

Lemont, Illinois

Founded

1946

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Simplify Jobs

Simplify's Take

What believers are saying

  • DOE secured $37 million for AI for Science, enabling virtual X-ray and microscopy environments across five labs.
  • Solstice and Equinox systems will deploy 110,000 NVIDIA Blackwell GPUs, creating the largest AI supercomputer in the DOE complex.
  • Chain Reaction Innovations incubator selected five clean energy startups in 2026 to scale scientific discoveries into businesses.

What critics are saying

  • DOE budget reallocation to Genesis Mission reduces ANL's standalone AI accelerator funding within 6–12 months.
  • SambaNova SN40L supply chain fragility threatens Metis cluster scalability and inference service uptime in 9–15 months.
  • Open-source ChemGraph allows private rivals to replicate AI-computational chemistry workflows without facility access.

What makes Argonne National Laboratory unique

  • Argonne operates the Aurora exascale supercomputer, ranked No. 1 in AI benchmarks for drug discovery.
  • The lab offers unique AI inference services on spare GPU capacity from Sophia and Metis clusters.
  • Argonne leads autonomous discovery with Polybot, an AI-guided robotic lab reducing material experiments from 4,300 to 64.

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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

Headcount

6 month growth

0%

1 year growth

0%

2 year growth

1%
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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.

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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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