Datatron Technologies

Founded 2016
16-50 employees
  • Technology Infrastructure
  • Information Systems
  • Analytics & Business Information
  • Headquarters address
    185 Clara St. Suite 101A, San Francisco, CA 94107

    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.

    Tech stack

    Python, SQL, AngularJS, Scala, Flask, Restful APIs, Spark, Kafka, Celery, RabbitMQ, Apache Airflow, Apache Flink, ElasticSearch, Docker, Kubernetes, Redshift, Postgres, Redash, AWS, Azure, Nginx


    Health and wellness

    Health insurance
    Dental insurance
    Vision insurance

    Values and quality of life

    Accessible via public transportation
    Snacks and beverages
    Catered lunches
    Catered dinners
    Company activities