Job Description

Nurix Therapeutics is a clinical stage biopharmaceutical company focused on the discovery, development, and commercialization of targeted protein degradation medicines, the next frontier in innovative drug design aimed at improving treatment options for patients with cancer and inflammatory diseases. Powered by a fully AI-integrated discovery engine capable of tackling any protein class, and coupled with unparalleled ligase expertise, Nurixs dedicated team has built a formidable advantage in translating the science of targeted protein degradation into clinical advancements. Nurix aims to establish degrader-based treatments at the forefront of patient care, writing medicines next chapter with a new script to outmatch disease.

Position

Nurix is seeking a highly accomplished and visionary Senior Machine Learning Scientist to lead the development and advancement of our cutting-edge ML platform. This critical role will be instrumental in driving innovation in drug discovery by leveraging the power of machine learning techniques to accelerate our targeted protein degradation (TPD) research programs. The successful candidate will provide strategic guidance for the application of our existing machine learning platform while spearheading research and integration of advanced methods such as foundational models and LLMs. You will possess a strong track record of research and development in machine learning, with a deep understanding of both fundamental concepts and their practical applications within the life sciences.

Responsibilities

• Lead the development and implementation of a state-of-the-art machine learning platform to support all stages of the TPD drug discovery pipeline.
• Research and develop novel machine learning methodologies , including foundation models, LLMs, and other cutting-edge techniques, and guide their application to Nurix degrader discovery programs.
• Guide the design and implementation of advanced AI/ML algorithms for tasks such as molecular design, property prediction, target identification, and lead optimization.
• Collaborate closely with research teams to identify and translate critical scientific challenges into tractable machine learning problems.
• Mentor junior scientists and foster a culture of innovation and excellence within the machine learning team.
• Stay at the forefront of cutting-edge research in machine learning, attending conferences, reviewing publications, and actively participating in the scientific community.

Desired Qualifications

• Ph.D. in Machine Learning, Computer Science, Bioinformatics, Computational Biology/Chemistry, Statistics, or a related scientific field with appropriate experience.
• Extensive experience (5+ years) in machine learning research and development within the biotech or pharmaceutical industries , with a proven track record of successful project delivery.
• Strong foundation in core machine learning and deep learning algorithms , including supervised, unsupervised, reinforcement learning, and their application in drug discovery or life sciences contexts.
• Proficiency in Python and experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Track record of leading and collaborating on machine learning projects from conception to adoption including model architecture, training, fine-tuning, and deployment. Preferred experience in GANs, VAEs, foundation models, reinforcement learning, LLMs, and their applications.
• Experience with scientific data , including chemical, biological, and/or genomic data.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and high-performance computing.
• Experience with MLOps practices (e.g., CI/CD, model monitoring, explainability).
• Excellent communication, presentation, and interpersonal skills with the ability to effectively communicate complex technical concepts to both technical and non-technical audiences.
• Strong leadership and mentorship skills with the ability to guide and motivate junior team members.
• A strong publication record in top-tier machine learning conferences or peer-reviewed journals.

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