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

We are working on an ambitious new platform that provides AI capabilities to all JetBrains products. Our platform is based on models developed in-house for writing and coding assistance, as well as integration with our strategic partners.

We are looking for a Research Engineer who can contribute to training foundation models for coding tasks. You’ll be working on developing Large Language Models from scratch and deploying them into production environments where they will be accessible by end users across the globe.

We value engineers who:

Can plan projects and make decisions independently, consulting with others if needed.
Identify customer needs and prioritize their tasks accordingly.
Start with the simplest solutions and gradually add complexity as needed.
Take sole responsibility for an entire subsystem.
Have a passion for learning and a desire to stay up to date with the latest developments in the LLM field.

In this role, you will:

Work with stakeholders to convert business requirements into technical specifications.
Train LLMs from scratch on a large GPU cluster.
Collect and process pre-training and fine-tuning datasets.
Support and improve existing subsystems.

We’ll be happy to have you on our team if you have:

Experience in design, deployment, and support of production ML systems.
A strong theoretical background in NLP and transformer-based approaches.
Proficiency with modern deep learning frameworks such as PyTorch and common libraries for NLP.
Experience in distributed training of multi-billion parameter models.
Attention to detail in everything you do and great communication skills.

We’d be especially thrilled if you have experience with:

LLM inference frameworks such as vLLM, DeepSpeed, TensorRT.
LLM alignment techniques such as RLHF/RLAIF.
MLOps tools and practices, including CI/CD for ML.
K8s and Kubeflow.
Scientific publications in the NLP field.

How we develop JetBrains AI:

A cluster of hundreds of NVIDIA GPUs as training infrastructure.
Git for source control management.
Python, PyTorch, and HuggingFace as an ML stack.
Kubeflow and Weights & Biases for experiment tracking.
TeamCity as a CI Automation system.

Staff Research Engineer (LLM Pre-Training)