About the role

Machine Learning Engineer specializing in large language models for John Snow Labs. Working on AI model training and optimization for healthcare applications in a fully remote setting.

Responsibilities

Adapt LLMs to diverse healthcare use-cases using techniques such as Sparse Fine-Tuning (SFT), Prompt Engineering Fine-Tuning (PEFT), Direct Parameter Optimization (DPO), and Proximal Policy Optimization (PPO).
Optimize LLMs for Retriever-Augmented Generation (RAG) to enhance decision-making and information retrieval capabilities.
Collect, clean, and refine healthcare datasets for training LLMs to ensure high-quality data provisioning.
Convert models into various formats suitable for production environments, ensuring their readiness for real-world application.

5+ years of hands-on professional experience in software engineering, building production-grade deep learning solutions.
An academic degree in computer science, data science, or a related degree. M.Sc. or Ph.D. degree is strongly preferred.
Demonstrated expertise in model tuning frameworks like Axolotl.
Familiarity with model serving frameworks, including vLLM, TGI, and llama-cpp, to support the deployment and scalability of machine learning models.
Knowledge of model quantization techniques and frameworks to optimize AI models for performance in resource-constrained environments.
Hands-on experience with Transformer architectures and proficiency in machine learning frameworks such as PyTorch.