Senior Machine Learning Scientist (UK Remote)

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 8 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.

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.