Top Benefits
Fully virtual company
About the role
Who you are
- The ideal candidate will have a robust background in machine learning, natural language processing (NLP), and Large Language Models (LLMs)
- 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
- This role offers an opportunity to be at the forefront of technological advancement in AI and healthcare, contributing to innovations that advance the healthcare, life science, and open-source AI communities. When applying, please add a cover letter which includes the words ‘John Snow Labs’ and explains your academic, professional ML engineering, and recent LLM engineering experience
What the job involves
- We are seeking a highly skilled and experienced Machine Learning Engineer to join our team
- The role focuses on training, tuning, and evaluating AI models as part of a diverse team which includes professionals from software engineering, data science, and medicine
- 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
Benefits
- Fully virtual company
Top Benefits
Fully virtual company
About the role
Who you are
- The ideal candidate will have a robust background in machine learning, natural language processing (NLP), and Large Language Models (LLMs)
- 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
- This role offers an opportunity to be at the forefront of technological advancement in AI and healthcare, contributing to innovations that advance the healthcare, life science, and open-source AI communities. When applying, please add a cover letter which includes the words ‘John Snow Labs’ and explains your academic, professional ML engineering, and recent LLM engineering experience
What the job involves
- We are seeking a highly skilled and experienced Machine Learning Engineer to join our team
- The role focuses on training, tuning, and evaluating AI models as part of a diverse team which includes professionals from software engineering, data science, and medicine
- 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
Benefits
- Fully virtual company