About the job
Join DeepWalk as a Senior/Staff Software Engineer
DeepWalk is an innovative startup dedicated to enhancing urban safety through cutting-edge computer vision technology. Our mission is to assist municipalities in monitoring and improving sidewalk conditions, ensuring safer pathways for all. We are proud to have established partnerships with numerous cities, universities, and engineering firms, making a tangible impact across the nation.
Over the past year, we have successfully processed thousands of miles of sidewalks across more than 20 states, generating millions of labeled data points that inform crucial infrastructure decisions. With a recent $2.1M seed funding round led by Enable Ventures, we are on a strong growth trajectory and are generating 7-figure revenues as we support communities across America.
Your Role
We are seeking a Senior/Staff Software Engineer to spearhead the technical evolution of our computer vision platform dedicated to automated sidewalk inspections. This role involves addressing complex, open-ended challenges associated with large-scale imagery, production-level ML systems, and extensive data pipelines that manage hundreds of terabytes of data. Ideal candidates will have substantial experience in developing and maintaining ML or computer vision systems in a production environment.
In this pivotal position, you will contribute significantly to the analysis and processing of millions of images, enabling cities to create safer and more accessible infrastructures. You'll collaborate closely with a talented team of engineers, guiding the technical direction and establishing scalable practices.
Our technology stack primarily consists of Python-based computer vision and machine learning systems hosted on AWS, complemented by robust data pipelines tailored for high-volume imagery and geospatial data management.
Your Responsibilities
- Oversee the entire lifecycle of our computer vision models, including their training, evaluation, deployment, and continuous improvement.
- Enhance model performance under real-world conditions, addressing challenges such as noise, edge cases, and data drift.
- Design and optimize data pipelines to efficiently process thousands of miles of sidewalk data and millions of images.
- Lead architectural strategies for managing large volumes of geospatial and visual data, focusing on storage organization, pipeline reliability, and inference efficiency.
- Establish and refine best practices for model deployment and system architecture.
