Type: Full TimeMin. Experience: Experienced
H2O.ai is the company behind H2O, the premier open-source machine learning platform that is transforming how enterprise AI applications are built. H2O is a distributed machine learning platform designed for big data with APIs are available in R, Python, Scala, Java and REST/JSON. With H2O, data scientists can take sophisticated models all the way to production using the same user-friendly platform that they use for modeling. Our customers have built mission critical applications across all industries including healthcare, insurance, finance, telecommunications and retail. The H2O.ai team is a mix of software, machine learning, and data science experts working together with the open source community to build H2O.
Founded in 2012, H2O.ai was one of the first companies to create an enterprise machine learning platform and continues to lead in this space.
Quality in H2O.ai is collectively owned by all engineers, with the Quality Engineering team being accountable for the efficiency of the code-submission/testing/release process. The QE team provides the right set of testing/integration/debugging/benchmarking tools to enable the developers to execute faster and own product quality.
We are looking for developers in three key areas with a passion for testing and learning new tools/technologies/concepts to work on our industry leading automated machine learning product: Driverless AI. This involves developing frameworks, tools and test automation for automatic feature engineering, machine learning interpretability, automatic scoring pipelines, datasource compatibility, cloud deployment, and many more. In addition to these, you will devise test strategies for new features, develop test plans, review test coverage, work with support and development teams to address customer needs, define release criteria for features.
Strong coding skills in Python, Java or C++
Proficient in a scripting language like Bash or Groovy
Bachelor's degree or higher in Computer Science or related field.
Experience with test automation and continuous integration.
Nice to have:
Experience with Docker, Linux, Jenkins, Git, machine learning algorithms, statistics, distributed/parallel systems.