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Graph Neural Network Internship Jobs in Chicago, IL

Our work spans networking, security, observability, and customer experience - designing and ... Large-scale graph representation learning and Graph Neural Networks (GNNs) (e.g., GCN/GAT/GraphSAGE ...

Product Compliance Intern

Itasca, IL · On-site

$38K - $47K/yr

Interns may gain experience with: * RoHS Compliance, REACH Compliance, PFAS Regulations, TSCA ... neural network processors. Syntiant also provides compute-efficient software solutions with ...

Product Compliance Intern

Itasca, IL · On-site

$38K - $47K/yr

Interns may gain experience with: * RoHS Compliance, REACH Compliance, PFAS Regulations, TSCA ... neural network processors. Syntiant also provides compute-efficient software solutions with ...

Product Compliance Intern

Itasca, IL · On-site

$38K - $47K/yr

Interns may gain experience with: * RoHS Compliance, REACH Compliance, PFAS Regulations, TSCA ... neural network processors. Syntiant also provides compute-efficient software solutions with ...

Graph Neural Network Internship information

See Chicago, IL salary details

$9

$17

$26

How much do graph neural network internship jobs pay per hour?

As of Aug 24, 2026, the average hourly pay for graph neural network internship in Chicago, IL is $17.96, according to ZipRecruiter salary data. Most workers in this role earn between $14.86 and $19.81 per hour, depending on experience, location, and employer.

What is a graph neural network internship?

A Graph Neural Network (GNN) Internship is a position designed for students or recent graduates to gain hands-on experience working with GNNs, a type of deep learning model that processes data structured as graphs. Interns typically participate in research, model development, and the application of GNNs to various problems such as social network analysis, recommendation systems, or molecular property prediction. The internship provides opportunities to collaborate with experienced researchers, learn cutting-edge techniques, and contribute to real-world projects involving graph-based machine learning.

What types of projects or tasks can I expect to work on during a graph neural network internship?

As a Graph Neural Network (GNN) intern, you will typically be involved in projects such as developing and optimizing GNN models for real-world datasets, implementing new neural network architectures, and conducting experiments to evaluate model performance. You may also assist with data preprocessing, feature engineering, and collaborating with data scientists and machine learning engineers to integrate GNN solutions into larger systems. Regular tasks include reviewing recent research, documenting findings, and presenting your results to the team. This internship offers an excellent opportunity to deepen your understanding of advanced machine learning methods while gaining hands-on experience in a collaborative research-focused environment.

What are the key skills and qualifications needed to thrive as a graph neural network intern, and why are they important?

To thrive as a Graph Neural Network Intern, you need a solid background in machine learning, data science, and programming languages such as Python, often supported by coursework or research experience in deep learning and graph theory. Familiarity with frameworks like PyTorch Geometric, TensorFlow, and libraries such as NetworkX, along with experience using Jupyter Notebooks and Git, is typically expected. Strong analytical thinking, problem-solving skills, and effective communication help interns collaborate with research teams and convey complex ideas clearly. These skills and qualifications are essential for contributing to cutting-edge AI projects and advancing research in graph-based machine learning.

What cities near Chicago, IL are hiring for Graph Neural Network Internship jobs?

Cities near Chicago, IL with the most Graph Neural Network Internship job openings:

Infographic showing various Graph Neural Network Internship job openings in Chicago, IL as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 13% Part Time, and 5% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution, with an average salary of $37,358 per year, or $18 per hour.

Hardware Machine Learning PhD Research Internship

IMC

Chicago, IL • On-site

$225K/yr

Full-time, Internship

PTO

Re-posted 9 days ago


Job description

We are deploying machine learning directly onto custom hardware - and we want you to help drive it forward. This PhD internship is an opportunity to work on research that has direct impact on IMC's work tackling open problems at the frontier of low-latency ML inference and hardware acceleration.

You'll work alongside IMC engineers in one of the most demanding low-latency computing environments in the world. You'll own a focused research project from start to finish, present your findings to the team, and leave behind a prototype or benchmark that we can build on.

Your Core Responsibilities

  • Architect and develop an ML focused research project based on real-world use cases
  • Work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions
  • Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems
  • Present your project to the team, deepening our collective understanding of an area of ML acceleration
  • Gain hardware design fundamentals from skilled RTL developers and learn how they apply to our industry
  • Build skills to evaluate research not only from an academic perspective, but through real-world performance constraints, engineering costs, and industry impact

Your Skills and Experience

  • Currently enrolled in a PhD program in Electrical Engineering, Computer Science, Physics, or a related field
  • Solid understanding of hardware constraints and design trade-offs (e.g., pipelining, resource utilization, fixed-point arithmetic) that shape how ML models can be efficiently mapped onto FPGAs or custom ASICs
  • Experience with hardware fundamentals, whether through VHDL/SystemVerilog development, HLS tools, or ML-to-hardware frameworks like hls4ml, FINN, or Vitis AI
  • Understanding of machine learning fundamentals - neural network architectures, inference optimization, quantization techniques, ML frameworks such as PyTorch/TensorFlow
  • Proficiency in Python or similar languages for tooling, testing, and simulation
  • Strong communication skills and ability to work collaboratively across disciplines with both technical and non-technical teams

You may submit one application per role each year.  We strongly encourage you to focus on applying to a single role that best matches your skills and interests.  Though you may apply to multiple roles, please note that each application will be evaluated based on the specific criteria established for that particular role. If you have already applied for this position during the current recruitment season and were not selected, you may reapply when the next recruitment season begins in 2027.

The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information.

Base Salary: $225,000