Staff Research Engineer (LLM Pre-Training)

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.

#LI-KP1

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