About the Role
At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving 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. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs. The ML Workflows Engineering team is dedicated to removing infrastructure challenges, streamlining machine learning operations (MLOps), and enabling teams to focus on the innovative work that matters most – building impactful ML models and intelligent agents. As part of the team, you'll play a key role in designing tools, automation, and pipelines that make machine learning development seamless and intuitive. By integrating cutting-edge MLOps practices and engineering excellence, we aim to maximize productivity and remove the complexity of ML infrastructure so that our teams can push the boundaries of what’s possible in AI.
Responsibilities
- Build tools, automation, and workflows to simplify infrastructure-heavy tasks, empowering AI teams to focus on experimentation and solving core challenges.
- Develop robust monitoring, logging, and tracing systems to ensure the performance and reproducibility of ML workflows in production.
- Design, implement, and maintain end-to-end machine learning pipelines to enable the seamless development, training, and deployment of ML models and intelligent agents.
- Work with large-scale distributed systems, including GPU clusters, to support training, fine-tuning, and evaluation of ML models.
- Collaborate with product and development teams to transform high-level goals into concrete, scalable, and maintainable systems.
- Optimize workflows for reproducibility, scalability, and cost-efficiency while keeping ML teams productive and focused on innovation.
Requirements
- Hands-on experience with modern MLOps tooling, including Kubernetes, Cloud providers (GCP and AWS), and ML orchestration frameworks.
- A solid understanding of the ML lifecycle from idea to the customer-facing application.
- The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
- A customer-centric mindset – you care about how ML engineers are actually working and can translate their needs into actionable, scalable, and maintainable architectural decisions.
- Experience with modern CI/CD systems, like GitHub Actions or JetBrains TeamCity.
- At least three years of Python experience writing clean, maintainable code in modern ML codebases.
Ideal Candidate Would Have Experience With
- ML orchestrators and workflow tools such as ZenML, Dagster, and Airflow.
- Developing infrastructure components and services using cluster solutions like Kubernetes.
- The development of Python-based backend services.
- Creating and maintaining ML pipelines, including legacy ones.
- Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.
Especially Thrilled If You Have Experience With
- LLM inference frameworks such as vLLM, DeepSpeed, and TensorRT.
- Writing and maintaining Python libraries used by internal (or external) ML engineers.
- A strong theoretical background in NLP and transformer-based approaches.
- Writing code in Java and/or Kotlin.