
We’re on a mission to redefine how modern distributed systems are tested and released. Our platform is trusted by engineering teams who demand rock-solid reliability, scalable performance, and deep technical visibility. Our platform doesn’t just assure system correctness and reliability, it exists because developers need something better. If you’ve ever experienced the pain of a production outage, had a bad week on-call, or had a release delayed by weeks because of one killer bug – you’ll understand exactly why we’re doing what we do. If you're passionate about developer-first products, system resilience, and correctness, we’d love to talk.
We’re hiring an Applied AI Software Engineer to build AI-native products to help our go-to-market and marketing teams.
You’ll work at the intersection of software engineering, applied AI, data, and GTM operations.
Why Join Us?
Our current stack for internal GTM applications includes TypeScript, React, LangChain, and AWS. Experience with these tools is helpful, but we care more about strong engineering fundamentals and the ability to learn.

Ship confidently and free your engineering team to focus on creating business value. Antithesis enables you to find and eliminate bugs that cause system downtime and difficult-to-fix correctness issues.
Our platform combines AI with advanced testing methodologies to thoroughly explore distributed systems and find the bugs that other tools miss. The platform perfectly reproduces bugs and the conditions that led to them, dramatically reducing the time required to debug systems and enabling engineers to validate fixes with 100% certainty.
Instead of having engineers (or AI) write more test cases, the platform enables property-based testing where you define desired outcomes. Our autonomous AI-powered testing engine explores millions of code paths in parallel and smartly injects the kind of faults your code routinely experiences in hostile production environments. No more testing only the "happy path" and missing the issues that will haunt you in production.