ML/AI Engineering

Hop develops intelligent systems at scale – reliably, reproducibly and responsibly

Here at Hop, we build production-scale systems to deploy machine learning at scale – whether that’s on premise, in the cloud or on device.

Sometimes, this also requires us to construct novel compute substrates to explore more interesting research questions. Our engineers focus on questions of scale, latency, concurrency and resilience. Though we have preferences, we’re generally language- and platform-agnostic, and have worked deeply in AWS, GCP and Azure, as well as various on-premise installations.

Featured Case Study

Toyota Research Institute’s Human Interactive Driving research team faced challenges as their experiments grew in complexity and scale. Engineering needs of the researchers outpaced their existing capabilities. Experimental datasets were approaching hyperscale levels. They needed advanced engineering and operations support.

Does your research team need additional ML/AI engineering capacity? Contact us to learn how Hop can help.