Engineering at Databricks
Iterate rapidly from idea to production
At Databricks, our engineers constantly push the boundaries of data and AI technology. We build highly technical products that help data teams solve the world’s toughest problems — from cybersecurity threat detection to cancer drug development to pioneering the next mode of transportation.
Our new open LLM, called DBRX, provides the open community — and enterprises building their own LLMs — with capabilities that were previously limited to closed model APIs.
If you’re looking for an opportunity that could define your engineering career, Databricks is it.
Culture built by engineers.
For engineers.
Founded in 2013 by the original creators of Apache Spark™, Delta Lake and MLflow, Databricks created a new category called data intelligence.
We’ve become the industry leader serving over 60% of the Fortune 500, where data, analytics and AI work together.
Make your mark in generative AI. Build the future of LLMs.
The majority of large companies store their data on Databricks, which makes us one of the largest data companies in the world. Every one of these companies plans to innovate their products and services using LLMs.
It’s an exciting time to be an engineer at Databricks. We’re building an entire application stack that helps companies build and serve their own instruction-following models.
Databricks is building a world-class team of engineers to work on LLM challenges such as:
- Large-scale distributed model training
- Optimizing GPU model serving
- Developing novel modeling methodologies that scale
- Production use cases
Do you have what it takes to be a founding member of the LLM team at Databricks? We want to hear from you.
Meet the engineers of Databricks
Find out what inspires our engineers
To learn more, check out our Mosaic Research Blog and Technical Blog
The platform teams are responsible for datacenter-scale infrastructure with millions of virtual machines under management, supporting hundreds of thousands of customers. These teams build scalable infrastructure for building, deploying, monitoring and operating fleets of services across multiple cloud platforms (AWS, GCP and Azure). Areas of focus include the core application architecture, distributed systems, networking, security and API infrastructure.