Own the lifecycle of our computer vision models, including training, evaluation, deployment, and iteration
Improve model performance in real-world conditions (noise, edge cases, data drift)
Design and improve data pipelines that process thousands of miles of sidewalks and millions of images
Lead architectural decisions for handling hundreds of terabytes of geospatial and visual data, including storage layout, pipeline reliability, and inference performance
Set and evolve best practices around deployment, observability, system reliability, and scalability
Act as a senior individual contributor and mentor, helping raise the technical bar across the engineering team
Work closely with the CEO and operations team to turn business needs into clear technical priorities

5+ years of experience, including direct ownership of production ML or computer vision systems (training, deployment, and ongoing operation)
Experience taking models from training → production → monitoring → iteration in a real-world environment
Experience owning system architecture and influencing technical decisions across teams
Experience deploying ML systems in real-world production environments
Fluency in at least one modern backend language (Python, Java, TypeScript, Go, etc.)
Strong understanding of system design, scalability, and distributed systems
Experience with cloud platforms such as AWS, GCP, or Azure
Comfort working in a startup or growth-stage environment with changing requirements

Senior/Staff Software Engineer – Machine Learning

Key skills