AI Research Scientist II – LLM

AI Research Scientist II – LLM

Requirements

• A Master’s Degree in Computer Science, Machine Learning, Statistics, Applied Mathematics or an equivalent highly technical field
• 3+ years of combined academic and industrial research experience developing LLM and other NLU models
• Drive one or more phases of the ML development lifecycle: shape datasets, investigate modeling approaches and architectures, train/evaluate/tune models and implement the end-to-end training pipeline
• Experience in big data ML as well as data efficient ML that leverages techniques such as synthetic data construction, transfer learning, active learning, semi-supervised learning, few-shot learning
• Hands on experience in developing, scaling and implementing machine learning solutions using relevant programming languages (such as Python), state-of-the-art deep learning frameworks (such as PyTorch and Tensorflow) and code development and review tools (such as Github)
• Experience in prompt engineering
• Experience in finetuning ML models
• Experience in developing LLM-based applications including agent-based systems, RAG-based systems
• Be Familiar with NLU/LLM cloud services and APIs (such as from OpenAI)
• Deep understanding of metrics for offline and online evaluation of LLM-based systems
• Track record of publications and contributions to the machine learning community
• Experience with designing and shipping software products that leverage machine learning at scale
• Excellent problem solving skills and ability to dive into learning optimization, model architecture, evaluation metrics, and field testing scenarios
• Comfort communicating and interacting with scientists, engineers and product managers as well as understanding and translating the science of AI and Machine Learning to a more general audience.

Responsibilities

• Drive one or more phases in ML development: shape datasets, investigate ML architectures, train/evaluate/tune ML models, implement end-end pipeline
• Leverage state-of-the-art research to deliver high quality models enabling multiple AI projects at scale
• Contribute back to the research community via academic publications, tech blogs, open-source code and contributing to internal/external AI challenges.

Preferred Qualifications

• A Ph.D. Degree in Computer Science, Machine Learning, Statistics, Applied Mathematics or an equivalent highly technical field
• Be familiar with privacy-preserving ML and ethical AI techniques
• Demonstrated knowledge and experience with distributed machine learning and deploying models at scale in cloud environments (such as AWS, Microsoft Azure and Google Cloud)
• Familiarity with IoT/Edge AI and optimizing ML models to run on-device with constrained compute, power and latency budgets
• Familiarity with multi-modal AI development.