Machine Learning Engineer - Large Language Models
New
J
JobgetherHealthcare AI
Based in LuxembourgFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- At least 5 years of professional experience
- Required Skills
- Machine LearningPyTorchLLM
Requirements
- At least 5 years of professional experience developing production-grade software engineering and deep learning solutions.
- Degree in Computer Science, Data Science, Machine Learning, or a related technical field; a Master's or Ph.D. is strongly preferred.
- Proven experience working with Large Language Models, including model fine-tuning and optimization techniques.
- Hands-on expertise with frameworks such as Axolotl and strong knowledge of Transformer-based architectures.
- Experience with machine learning frameworks, particularly PyTorch.
- Familiarity with model serving technologies such as vLLM, Text Generation Inference (TGI), and llama.cpp.
- Understanding of model quantization techniques and performance optimization for resource-constrained environments.
- Strong problem-solving, collaboration, and communication skills with the ability to work effectively in distributed teams.
- Passion for AI innovation and applying machine learning to impactful real-world use cases.
Responsibilities
- Fine-tune and optimize Large Language Models for a variety of healthcare and life science applications using techniques such as Supervised Fine-Tuning (SFT), Parameter-Efficient Fine-Tuning (PEFT), Direct Preference Optimization (DPO), and Proximal Policy Optimization (PPO).
- Develop and enhance Retrieval-Augmented Generation (RAG) pipelines to improve the accuracy, relevance, and efficiency of AI-powered information retrieval systems.
- Collect, prepare, clean, and curate high-quality datasets to support the training, evaluation, and continuous improvement of machine learning models.
- Convert, optimize, and package AI models for production deployment across different serving environments while ensuring scalability and performance.
- Collaborate with cross-functional teams to build, evaluate, and deploy robust deep learning solutions that address complex healthcare challenges.
- Stay up to date with emerging advancements in LLMs, machine learning infrastructure, and AI engineering best practices to continuously improve model performance and reliability.
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