About the role
About Us At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges. From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future. About the Deep Learning Team The Deep learning team’s work is at the crux of Hayden AI’s solutions to its customers.
The team is responsible for building and maintaining advanced models that are able to perceive the world and the knowledge produced by the models is used downstream to implement various customer use-cases.
The team runs a number of models both in the cloud as well as on the edge device serving different scenarios.
The team also works closely with the platform team to build out Hayden AI’s MLOps infrastructure to achieve better scale and reliability.
About
the Role As a Deep Learning Engineer at Hayden, you will make key contributions towards building foundational AI capabilities for Hayden that will enable solving complex problems for Hayden AI Customers. In this role, you will build, train, evaluate and deploy state of the art ML models in production at scale. You will report into the Director of AI within the Perception Org and work closely with other engineering as well as product teams to deliver value to Hayden AI customers. We are looking for someone who has familiarity with or growing expertise in at least one of the verticals below — depth in a specific area is valued but not required on day one. 3D vision models to predict depth and 3D structure, Video/temporal behavior models to predict intent, Deep understanding of Vision language models and hands-on experience fine tuning them, Deep understanding of foundation models in perception. Deep knowledge of Nvidia edge device stack for running ML models and cuda know-how in terms delivering highly optimized models This position is based in San Francisco and follows a hybrid schedule with at least 3 days in-office per week. Key
