Software Engineer (Tooling for ML/AI Engineers)

At JetBrains, code is our passion. Ever since we started back in 2000, we have been striving to make the most effective developer tools on earth. Our tools speed up production by automating routine checks and corrections, freeing developers to grow, discover, and create.

Cadence is a product designed to transition local machine learning (ML) experiments to the cloud seamlessly. With Cadence, users can access the resources they need directly from their local environment, streamlining workflows for ML experiments, data processing, and other resource-intensive tasks. We’re looking for experienced software engineers who thrive in a startup atmosphere and are excited to help drive new feature development and expand support for a wider range of user scenarios.

Why you’ll love this role: If you’re an engineer with an eye on AI but a heart for scalable infrastructure, this role combines the best of both worlds. You’ll drive the development of cutting-edge tools to support ML/AI workflows while designing high-performance infrastructure that ML engineers can trust and love.

In this role, you will:

• Develop ML tooling: Build tools to support ML/AI workflows, focusing primarily on training but extending it as needed.
• Optimize infrastructure: Design and build scalable infrastructure for efficient ML pipelines.
• Ensure seamless integration: Collaborate with ML engineers to create an optimal experience in JetBrains IDEs, tailored to their specific needs.
• Monitor and improve: Establish benchmarks to assess tool performance and drive ongoing improvements.
• Stay up to date: Stay current with ML Ops / LLM Ops trends, identifying new tools to enhance engineering workflows.

We will be happy to have you on our team if you have:

• 3+ years of experience in backend or infrastructure development, ideally focused on tools or platforms for machine learning workflows.
• A strong understanding of cloud infrastructure and containerization to support scalable and robust ML pipelines.
• Knowledge of CI/CD and automation practices for building reliable, reproducible processes.
• Strong coding skills in Kotlin and Python with experience writing production-ready code.
• Excellent analytical and problem-solving skills.
• Strong spoken and written communication skills in English.

It would be great if you also have:

• Experience with ML Ops tools like DVC, Kubeflow, MLflow, or similar.
• Experience building IDE plugins.