Senior Backend Software Engineer, ReadySet

$160-210k

+ Equity

AWS
GCP
Python
Azure
Junior and Mid level
Remote from US

More information about location

ReadySet

SQL caching engine

Job no longer available

ReadySet

SQL caching engine

21-100 employees

B2BEnterpriseBig dataAnalyticsSaaSData Analysis

Job no longer available

$160-210k

+ Equity

AWS
GCP
Python
Azure
Junior and Mid level
Remote from US

More information about location

21-100 employees

B2BEnterpriseBig dataAnalyticsSaaSData Analysis

Company mission

To build the future data layer of the web.

Company mission

To build the future data layer of the web.

Our take

Once applications begin to gain popularity, databases need to be able to handle an increased volume of requests, larger data sets, and more complicated queries. Databases that can’t keep up can lead to substantial business losses precisely when there’s most to be gained: Super Bowl weekend, for example, or Black Friday. ReadySet is looking to help companies dodge this issue.

The company has developed a plug-and-play data caching layer that pre-computes and caches query results in relational databases, meaning that database reads can remain fast. In fact, ReadySet claims that the layer supports millions of reads per second “with sub-millisecond latencies on a single node”.

This is a hugely impressive feat from the company, founded by a team of data systems researchers from MIT, for whom ReadySet began as Noria - the open source streaming data-flow system developed in the lab. Plus, surveys suggest that there’s substantial enterprise appetite for this kind of product. This is partly thanks to unfit legacy systems, and partly in anticipation of the swelling user and data volumes anticipated for the years ahead - both are good news for ReadySet.

Freddie headshot

Freddie

Company Specialist at Welcome to the Jungle

Funding (1 round)

Apr 2022

$29m

SERIES A

Total funding: $29m

This company has top investors

Leadership

Alana Marzoev

(Founder & Chairman)

Previously worked on ML engineering at BioBots, then ML and Computer Systems Research first at UC Berkeley College of Engineering's RISELab, then at Cornell University College of Engineering. Subsequently worked in cloud infrastructure at Microsoft, and recently completed an MIT PhD in distributed systems and databases.