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Graph Neural Network Internship Jobs in Michigan

Cisco

Applied AI Scientist

Ann Arbor, MI · On-site

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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 ...

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Graph Neural Network Internship information

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.

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Graph Neural Network Internship jobs near you

Lead ML Engineer - Lane & Route Network Mapping

May Mobility

Ann Arbor, MI • On-site

$100K - $132K/yr

Full-time

Re-posted 19 days ago

Job description

Job Summary:
May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. They are seeking a Lead ML Engineer to architect the next generation of their mapping and localization stack, focusing on developing advanced neural networks for lane and route network mapping.
Responsibilities:
• Lead the research, design, architecture, training and validation of advanced neural networks for vectorized mapping (e.g., MapTR), multi-camera BEV transformers, and multimodal fusion models to extract and model lane and route networks for both high-fidelity offline pipelines and real-time online mapping.
• Architect, design, and implement a production-grade lane and route network mapping stack, ensuring high-performance integration with upstream and downstream modules like Perception, Behavior, Policy, and Prediction.
• Drive major feature development from inception to deployment. This includes high-level architecture design, rigorous code reviews, automated testing, mentorship of junior engineers, and technical resolution.
• Own the end-to-end data strategy for the mapping domain, specifically focusing on lane and route networks. You will define data curation, auto-labeling, synthetic data, and active learning pipelines to capture and resolve long-tail scenarios.
• Develop robust metrics and evaluation frameworks for lane and route network accuracy, temporal consistency, and scaling across diverse Operational Design Domains (ODDs).
• Work independently with cross-functional teams to translate complex autonomy goals into clear software and system requirements.
• Collaborate with ML and Autonomy engineers to ensure the seamless deployment and validation of mapping features to the vehicle fleet.
• Stay at the research frontier by evaluating, adapting, and innovating cutting-edge techniques, including online vectorized HD map construction, end-to-end mapping models, and vision/fusion Foundation Models to deliver production-ready solutions.
Qualifications:
Required:
• Ph.D. or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field with a strong mathematical and engineering foundation.
• 7+ years of industry experience developing and deploying ML/DL models for mapping or computer vision at scale.
• Deep expertise in several of the following areas: Vectorized mapping networks (e.g., MapTR), BEV-based scene representation, and temporal modeling.
• Cross-modal calibration and fusion (e.g., Camera-to-LiDAR) within Bird's-Eye-View (BEV) unified representation spaces.
• Transformers or Graph Neural Networks (GNNs) applied to structured lane geometry and topological connectivity.
• Lane-level topology and connectivity, intersection modeling, and lane/road network graph construction.
• Computer Vision Foundations: Object detection, classification, segmentation, tracking, depth estimation, and 3D reconstruction.
• Strong understanding of HD maps, including lane and road network geometry modeling, connectivity, and semantic attributes.
• Expertise in ML/DL development using PyTorch or TensorFlow, including experience with distributed training, synthetic data generation, large-scale dataset handling, and data curation strategies.
• Strong programming skills in Python and/or C++ with experience in modular software design and Linux-based development.
• Proven leadership in guiding technical roadmaps, mentoring engineers, and driving measurable improvements in model performance and system reliability.
• Strong communication skills with the ability to lead technical discussions and align with cross-functional teams.
Preferred:
• 10+ years of experience in ML/DL for autonomous driving or ADAS systems.
• Experience with self-supervised and/or semi-supervised learning for large-scale representation learning.
• Experience utilizing Vision-Language Models (VLMs) and/or Foundation Models for auto-labeling and long-tail (edge-case) detection.
• Expertise in ML optimization for real-time products with limited compute, such as quantization, pruning, or distillation of large transformer models.
• A proven record of inventions and/or publication record at top-tier conferences (e.g., CVPR, NeurIPS, ICCV, ECCV, ICLR).
Company:
May Mobility is a manufacturing firm that designs and develops autonomous technology vehicles for self-driving transportation solutions. Founded in 2017, the company is headquartered in Ann Arbor, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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