Who we are:
Glydways is reimagining what public transit can be. We believe that mobility is the gateway to opportunity—connecting people to housing, education, employment, commerce, and care. By making transportation more accessible, affordable, and sustainable, we empower communities to thrive and unlock economic and social prosperity.
Our mission is to revolutionize transit with a solution that delivers high capacity, exceptional user experiences, unmatched affordability, and minimal environmental impact.
The Glydways system is a groundbreaking network of carbon-neutral, interconnected transit pathways powered by standardized autonomous vehicles on dedicated roadways. Operating 24/7 with on-demand access, it offers personalized and efficient mobility—without the burden of heavy upfront infrastructure costs or ongoing taxpayer subsidies.
With Glydways, we’re building more than a transportation system; we’re creating a future where everyone, everywhere, has the freedom to move.
Meet the team:
We are looking for a highly motivated Perception Software Engineer with a strong background in machine learning to join our ML Perception team and apply your expertise to real-world multi agent autonomous driving and infrastructure challenges to help Glydcars see, and change the way people move around the world. In this role, you will focus on multimodal efficient deep learning for safety critical systems with built in redundancy and robustness. Collaborating with a multidisciplinary team, you will develop state of the art models and algorithms to enhance our perception stack.
Roles & Responsibilities:
- Develop and implement state-of-the-art optimize efficient real time onboard multimodal multitask ML perception models, and finetune to improve performance
- Apply techniques to optimize model inference running on Jetson, such as quantization, pruning, architecture search, etc.
- Develop CUDA kernels and TensorRT plugins to perform custom operations and pre-/post-processing
- Research and development in various areas, including multiview sensor fusion for redundancy, robustness, and safety critical operation; and scene understanding with a focus on anomaly detection.
- Design and perform experiments, and share and present findings, including publication.
- Work with multimodal (camera, lidar, radar) datasets from a multiagent autonomous system
Knowledge, Skills and Abilities:
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
- 3 years of professional experience in Machine Learning, preferably working in automotive applications and perception for autonomous driving
- Strong programming skills (Modern C++ and/or Python)
- Familiarity with deep learning frameworks such as Pytorch
- Familiarity with modern deep learning architectures including transformer based architectures
- Excellent communication and interpersonal skills;
- Candidates with experience in optimizing model inference and/or CUDA kernel development are preferred
Glydways provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
Top Skills
Similar Jobs
What you need to know about the Chicago Tech Scene
Key Facts About Chicago Tech
- Number of Tech Workers: 245,800; 5.2% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: McDonald’s, John Deere, Boeing, Morningstar
- Key Industries: Artificial intelligence, biotechnology, fintech, software, logistics technology
- Funding Landscape: $2.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Pritzker Group Venture Capital, Arch Venture Partners, MATH Venture Partners, Jump Capital, Hyde Park Venture Partners
- Research Centers and Universities: Northwestern University, University of Chicago, University of Illinois Urbana-Champaign, Illinois Institute of Technology, Argonne National Laboratory, Fermi National Accelerator Laboratory
