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Senior Software Engineer
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At JetBrains, code is our passion. Ever since we started, back in 2000, we have strived to make the strongest, most effective developer tools on earth. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.
Here in the AI Assistant division's Infrastructure team, our mission is to build reliable, efficient tools and platforms that support the necessary technical foundation for ML activities, including evaluation and supporting processes, for all AI Assistant product teams. We are currently looking for a Senior Software Engineer to help create the engineering environment that allows our ML experts to effectively test and validate their models.
In This Role, You Will
- Design and implement robust internal systems and tooling necessary for automating ML-related workflows, with a strong focus on evaluation and testing processes.
- Deliver high-quality, stable software solutions for infrastructure challenges, primarily using Kotlin and Python.
- Solve diverse infrastructure problems, write automation and integration scripts, and connect various internal and external services.
- Contribute to technical discussions and decisions regarding the structure and evolution of our team's platform.
- Work closely with ML engineers, product managers, software engineers, and QA specialists to understand their daily challenges and deliver solutions that improve their workflow efficiency.
- Conduct thorough code reviews and help maintain high engineering standards within the team.
We'll be happy to have you on our team if you have:
- At least 3 years of professional experience in software development.
- Strong experience with either Kotlin or Java and familiarity with Python.
- A solid understanding of standard software engineering practices, patterns, and testing methodologies.
- Experience with building and maintaining internal tooling or core infrastructure components.
- Experience with CI/CD tools (e.g. Jenkins, TeamCity, GitLab CI, etc.) and pipeline automation.
- Familiarity with basic infrastructure concepts (e.g. working with containers or Linux environments).
- The ability to write clean, maintainable code and effectively debug complex issues.
- Excellent communication skills and the ability to clearly document technical work.
We'll Be Especially Thrilled If You Have
- Prior experience in an infrastructure, platform, or a DevOps-focused team.
- Familiarity with the concepts of MLOps or the lifecycle of ML models (even without direct experience in training them).
- Experience with orchestration tools (e.g. Kubernetes).
