LLM Data Scientist

We are currently requesting resumes for the following position:  LLM Data Scientist

Resume Due Date:  Friday, January 12^th, 2024 (5:00PM EST)

Number of Vacancies: 1

Level:  MP4 (Senior)

Duration: 12 months

Hours of work:  35 hours a week

Location: 800 Kipling (Hybrid, 2 days remote)

Job Overview

As a LLM Model Fine-tuning Specialist, you will be responsible for advancing our language models’ capabilities by leveraging your expertise in fine-tuning LLMs. The ideal candidate will have extensive knowledge of Natural Language Processing, Deep Learning architectures, proficiency in utilizing both cloud and local hardware, and experience deploying models on commercial SaaS platforms such as Databricks and Azure. You will collaborate closely with our team to develop and deploy state-of-the-art models, contributing significantly to our product’s success.

• Lead the fine-tuning of Large Language Models (LLMs) to optimize performance across various NLP domains and tasks.
• Utilize expertise in deep learning and machine learning architectures to customize and optimize model’s performance in terms of accuracy and efficiency.
• Collaborate with cross-functional teams to integrate fine-tuned models into industrial-level applications and products.
• Evaluate and pre-process large-size text and numerical datasets for model training, ensuring high-quality inputs.
• Support the development and deployment of models on local hardware and commercial SaaS platforms like Databricks and Azure ML.
• Stay updated with the latest advancements in LLMs and implement best practices for model fine-tuning and NLP techniques.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related field (Ph.D. preferred).
• Proven experience (>3 years) in fine-tuning Language Models (LLMs) with a deep understanding of LLM architectures (e.g., GPT, BERT, Transformer).
• Extensive expertise in deep learning and machine learning, with a focus on natural language processing (NLP).
• Hands-on experience in developing and deploying industrial-level deep learning models and applications.
• Proficiency in utilizing local hardware for model training and deployment; experience with commercial SaaS platforms like Databricks and Azure ML is a strong plus.
• Strong programming skills in languages such as Python, familiarity with relevant libraries (TensorFlow, PyTorch, Hugging Face Transformers, etc.).
• Excellent problem-solving abilities and a proactive approach to addressing challenges.
• Strong communication skills and the ability to collaborate effectively in a team environment.