Datatron Technologies

We help enterprises harness the awesome but unwieldy power of machine learning

The ability of large companies to govern their machine learning (ML) models has outstripped their infrastructure and the bandwidth of their engineering and data science teams.
Making ML models isn't the problem – the problem, at bottom, is organization. The solution is Datatron's ML platform, which speeds up model deployment, detects problems early, and increases the efficiency of managing multiple models at scale.

What we do

Datatron helps data scientists and engineers deploy their data science workflow into production. The platform manages and orchestrates all steps – from data ingestion and transformation to model training and serving these models as scalable, fault-tolerant web services. We hope 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.
We have offices in both San Francisco and Mountain View (our HQ).

You're in good hands

Our team of data science and machine learning experts come from Snap, Twitter, Microsoft, Lyft, and Amazon. We’re helping enterprises solve the problems we experienced first-hand before—long, inefficient production iteration timelines and finger-pointing amongst engineering, DevOps and data science teams.
Our founders come with top-notch experience working in the industry. CEO Harish Doddi built Snapchat’s My Story infrastructure, and CTO Jerry Xu was a founding member of the Microsoft Azure team. Together, they built the industry’s first surge-pricing model at Lyft.

How we interview and hire

We begin with an introductory phone screen 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 call with a senior engineer. Lastly, we will invite you for a half-day onsite interview before we make our final decision.
This entire process, from the initial phone screen to the onsite, should take no more than 2 weeks.