We believe the future of software QA is autonomous testing.
Significant fractions of IT budgets are spent on software testing, but too many bugs are still discovered in the most expensive and frustrating way: when a customer reports them in the wild.
At our previous venture, distributed database company FoundationDB, we pioneered autonomous testing methods that could catch nearly all bugs before they hit production, improving our software quality and significantly decreasing the time we spent debugging.
At Antithesis, we want to bring the confidence and productivity benefits of autonomous testing to our customers. We have developed a platform that searches for bugs in customer's software, in a controlled environment where all bugs are reproducible. Our first offering finds fault tolerance bugs: failures of customer's software that occur under specific network, hardware or timing conditions.
We're looking for senior engineers to join our effort to build our autonomous testing product. As a Lead ML Engineer, you will work with a small, focused team on open-ended and novel applications of unsupervised representation learning. As part of Antithesis’s mission to revolutionize autonomous software testing, you will push the boundaries on fundamental questions of software exploration and guided fuzzing.
Like all engineers, we value multiple skills and interests and want all ML Engineers contributing to software development projects across the team.
We're doing things nobody has done before, so we don't require expertise with any specific language or framework, etc. Mainly we care that: you're smart, get things done, a joy to work with, and are comfortable dealing with open-ended/poorly-defined problems.
If that sounds like you, include with your resume a brief (one to two paragraph) explanation of an interesting technical challenge you've faced.