Staff Software Engineer, ReadySet
Cloud and AI
$190-240k
+ Equity
Database scaling platform & SQL query cache
Open for applications
Database scaling platform & SQL query cache
21-100 employees
Open for applications
$190-240k
+ Equity
21-100 employees
Company mission
To build a modern database scaling platform that sits in front of existing databases and dramatically improves database performance by accelerating reads.
Role
Who you are
- We’re looking for people who are excited about exploring and productionizing the frontier of distributed systems and DB research to join our fully-remote team. You’d be a great fit at Readyset if you're excited about bringing to market data infrastructure that makes applications faster, simpler, and easier to deploy
- 10+ years of software engineering experience, ideally with exposure to databases, data platforms, or large-scale systems
- Proven ability to design and run cloud services at scale with high availability (3–4 9s)
- Experience with AI/ML systems or integrating LLMs into production (retrieval pipelines, memory systems, agent frameworks)
- Expert backend development experience in Python, Golang, Java, Rust etc.; familiarity with modern frontend frameworks a plus
- Deep familiarity with AWS (experience with GCP/Azure a plus)
- Demonstrated technical leadership: shaping architecture, influencing design, and guiding teams through complexity
- A mindset for velocity with rigor: moving fast without compromising sound engineering
What the job involves
- We’re looking for a Staff Software Engineer to help build the next generation of AI-native cloud infrastructure: systems that power data-intensive products and bring advanced AI capabilities into production
- Our Cloud and AI team moves fast and delivers what others call impossible, shipping ambitious systems at breakneck speed while maintaining engineering rigor
- The challenges we’re tackling are the ones that define enterprise-scale AI systems:
- Freshness: ensuring data is always current, since stale results immediately erode user trust
- Memory: designing structured, retrievable memory so agents can behave reliably over time, not just like short-lived chatbots
- Cost: keeping re-embedding, reranker passes, and LLM calls sustainable through batching, caching, and smart model routing
- We believe solving these challenges is key to making AI systems production-grade: responsive, reliable, and affordable at scale. As a Staff Engineer, you’ll play a central role in designing and operating the infrastructure that makes this possible
- Design and own critical services: hands-on development of systems that are robust, scalable, and simple to operate in production
- Lead high-impact architectural decisions, balancing speed with sound engineering for 3–4 9s availability
- Work across the stack, from distributed cloud infrastructure to AI-driven features like retrieval, memory, and agent orchestration
- Collaborate with leadership and product teams to translate ambitious ideas into reality, fast
- Act as a technical leader: mentor engineers, drive engineering culture, and shape the way we build as we scale
- Contribute to Readyset’s engineering strategy and vision, not just individual projects
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
Company Specialist at Welcome to the Jungle
Company
Funding (1 round)
Apr 2022
$29m
SERIES A
Total funding: $29m
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.
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