What We Do
As Enterprises invest more in AI/ML, the need for automation of model management and AI operations in production becomes clear. Datatron is fulfilling this need by automating model deployment, model monitoring, and model governance with a platform that bridges the gap between engineers, data scientists, and business stakeholders in production. We manage and orchestrates all the steps from data ingestion and transformation, to model training and serving these models as scalable, fault-tolerant web services. Datatron‘s unique Machine Learning technology creates a bidirectional partnership between user and machine, with each component learning from the other and becoming smarter through use. Datatron helps data scientists and engineers deploy their data science workflow into production in hopes to free data scientists from writing more bash scripts or glue code and instead allow them to focus on feature and model building, thereby accelerating their development lifecycle.
At Datatron, we're committed to define and build the Machine Learning platform that will make models come to life in production en masse. We are defining an industry and a category. Our founders and technical advisors have extensive backgrounds in data science and infrastructure, and have worked together for years on the engineering and data science teams at Lyft, Twitter, Microsoft, Amazon, Oracle, and Snapchat. Today, we are a pre-Series A company onboard our first few customers. We work hard, and we're serious about what we do.
How We Interview & Hire
Our interview process begins with a quick phone call to help us learn more about you, and for you to learn more about our company and the position. If we both agree that you'd be a great fit for our company, we will proceed with a 1-hour technical screening with a senior engineer. Lastly, we will invite you for a half-day onsite interview before we make our final decision. The entire process from the initial phone screen to the onsite should take no more than 2 weeks.
Our office is located in Downtown San Francisco.