Layer

Machine Learning applications are becoming more popular everyday, however the process for developing, deploying, monitoring and improving them is more complex compared to more traditional software, such as a web service or a mobile application. They are subject to change in multiple axes: the code itself, the data and the ML model. Their behavior is often complex and hard to predict, and they are harder to test, harder to explain, harder to monitor and improve. Layer is building the Declarative Machine Learning platform to help data teams across all companies to produce machine learning applications based on code in small and safe increments that can be reproduced, tested and reliably released at any time, in short adaptation cycles. With DML you define what to accomplish, rather than describe how to accomplish it. You pass your dataset, feature and ml model definitions, and Layer builds your entities seamlessly. This enables you to focus on what matters to you - designing, developing and deploying models - without worrying about the infrastructure to do it. We are founded by a serial entrepreneur, who most recently sold his startup ($100M+ EBITDA run rate) and a partner at a top European VC. We are a team of ex-Google/Improbable/Palantir/Lightbend/Hazelcast/Amazon/DataRobot engineers. We are at an early stage with some pilot customers lined up and are well-funded. We have an all-remote culture as we aspire to work with the best talent, no matter where they live. We are currently looking to hire within the UTC to UTC+5 time zones but we are happy to make exceptions for truly extraordinary candidates (you built an MLOps platform or a Spark committer, for instance) We pay competitive salaries and give stock options, among other perks. Our tech stack is React/TypeScript on the frontend, Java at the backend (with some Python/Scala) -- we also use/love Terraform, Kubernetes, AWS, Spark. We are currently looking for experienced candidates for full-time roles (no part-time/contractors please). Prior startup experience, being a prolific open source contributor, having worked at a data related software company (MLOps/ETL/integration) are pluses. No recruiters/agencies/outstaffing or outsourcing companies please.