As a member of the C3 AI Data Science team, you will work with some of the largest companies on the planet helping them build the next generation of AI-powered enterprise applications on the C3 AI Platform (c3.ai/customers/). You will work directly with researchers, data scientists, software engineers, and subject matter experts in the definition of new generative AI capabilities able to provide our customers with the information they need to make proper decisions and enable their digital transformation.
Qualified candidates will have an in-depth knowledge of the most common Large Language Models (LLMs) and Retrieval Methods, know how to train and fine-tune LLMs, and design and implement LLM-powered agents and tools at scale.
Responsibilities: Design and deploy Generative AI solutions, such as information retrieval and coding assistance, for industrial customers. Collaborate with Generative AI subject matter experts from C3 AI, its customer teams, and academia to identify, design, and implement innovative and differentiated solutions using cutting-edge research on LLMs and Generative AI. Drive the adoption and scalability of Generative AI offerings within C3 AI products. Qualifications: MS or PhD in Computer Science, Electrical Engineering, Statistics, Robotics or equivalent fields. Applied Machine Learning experience (regression and classification, supervised, and unsupervised learning). Strong mathematical background (linear algebra, calculus, probability, and statistics). Proficiency in Python and object-oriented programming. Strong experience working with machine learning and natural language processing techniques and tools. Strong experience using Generative AI models, with a good understanding of deep learning model classes such as GPT, VAE, and GANs, as well as their hyperparameters. Strong experience with retrieval methods e.g. using embeddings. Strong experience using key Python packages for data wrangling, machine learning and deep learning such as pandas, sklearn, TensorFlow, torch, transformers, LangChain, etc. Experience in Prompt Engineering and few-shot techniques to enhance LLM's performance on specific tasks. Experience with training and fine-tuning deep learning models, especially LLMs, and how to tune hyperparameters to ensure task generalization. Ability to drive a project and work both independently and within a cross-functional team. Smart, motivated, can-do attitude, innovative and seeks to make a difference in a fast-paced environment. Excellent verbal and written communication, able to articulate complex concepts with a non-technical audience. Preferred Qualifications: Experience with embedding model training and retrieval method evaluation approaches. Experience with LLM architectures, adapters, Mixture of Experts (MoEs) pretraining and fine-tuning techniques. Experience with design, deployment, and evaluation of LLM-powered agents and tools and orchestration approaches. Experience with reinforcement learning approaches in the context of fine-tuning LLM outputs. Experience with time series analysis and multivariate time series modeling.
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