Principal Machine Learning Scientist

View full description

Requirements:

• Experience working with text data to build Deep Learning and ML models, both supervised and unsupervised. Experience with deep learning in other modalities such as vision and speech would be a strong bonus.
• A strong understanding of the math and theory behind machine learning and deep learning.
• Software engineering background with at least 3-5 years of experience (we use Python, SQL, Unix-based systems, git, and github for collaboration and review).
• Machine / Deep Learning development skills, including experiment tracking (we use AWS SageMaker, Hugging Face, transformers, PyTorch, scikit-learn, Jupyter, Weights & Biases).
• An understanding of Language Models, using and training / fine-tuning and a familiarity with industry-standard LM families.
• Master's degree or PhD in Computer Science, Electrical Engineering, AI, Machine Learning, applied math or related field, with relevant industry experience, or outstanding previous achievements in this role. A Computer Science background is required as opposed to statistics or pure mathematics. We’re an applied science group leaning towards deep learning and therefore software development proficiency is a prerequisite.
• Excellent communication and teamwork skills.
• Fluent in written and spoken English.

Responsibilities:

• Work with subject matter experts and product owners to determine what questions should be asked and what questions can be answered.
• Work with subject matter experts to curate, generate, and annotate data, and create optimal datasets following responsible data collection and model maintenance practices.
• Answer questions and make trainable datasets from raw data, using efficient SQL queries and scripting languages, visualizing when necessary.
• Develop and tune Machine Learning models, following best practices to select datasets, architectures, and model parameters.
• Utilize, adopt, and fine-tune Language Models, including third-party LLMs (through prompt engineering and orchestration) and locally hosted LMs.
• Stay current in the field - read research papers, experiment with new architectures and LLMs, and share your findings.
• Optimize models for scaled production usage.
• Communicate insights, as well as the behavior and limitations of models, to peers, subject matter experts, and product owners.
• Write clean, efficient, and modular code, with automated tests and appropriate documentation.
• Stay up to date with technology, make good technological choices, and be able to explain them to the organization.