Machine Learning Scientist

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, Nurix’s 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 medicine’s next chapter with a new script to outmatch disease.

Position 

Nurix is seeking a talented, inquisitive, and passionate Machine Learning Scientist to join our cutting-edge research team and revolutionize the development of Targeted Protein Degradation (TPD) therapeutics. The successful candidate will play a pivotal role in building and applying our robust ML Platform to accelerate drug discovery. This involves collaborating closely with interdisciplinary project teams, identifying critical research challenges amenable to machine learning solutions, developing high-quality predictive models, and deploying these models to streamline the drug design process. You will leverage our extensive DNA Encoded Library (DEL) and Degrader datasets to drive impactful decisions at every stage of the drug development pipeline 

Responsibilities 

• Develop and deploy high-performing machine learning models to address critical research needs within the TPD drug discovery pipeline.
• Apply Nurix's DEL ML and DEL Foundation platforms to guide Lead Identification and Lead Optimization efforts, extracting valuable insights from our experimental DEL platform.
• Design and conduct rigorous model validation and performance assessments to ensure the reliability and robustness of ML predictions.
• Collaborate effectively with cross-functional research teams (e.g., chemists, biologists, engineers) to translate research questions into actionable ML problems.
• Advocate for the adoption of machine learning solutions within the research organization, effectively communicating the value and impact of ML-driven insights.
• Maintain and enhance existing machine learning codebases, ensuring their scalability, maintainability, and performance.
• Stay abreast of the latest advancements in machine learning research and technologies, identifying opportunities to incorporate novel methods into our drug discovery efforts

 Desired Qualifications 

• Recent PhD or strong experience (MS + 1-3 years or BS + 3-5 years) in Machine Learning, Bioinformatics, Computational Biology, Computer Science, Statistics, or a related field.
• Demonstrated Python proficiency and experience with modern frameworks for Machine Learning: Proven experience implementing projects with supervised and unsupervised learning algorithms using modern python packages (e.g., linear regression, logistic regression, support vector machines, random forests, clustering, deep learning, scikit-learn, PyTorch, Tensorflow).
• Data analysis and visualization: Experience with data cleaning, preprocessing, exploratory data analysis, and visualizing results (e.g., pandas, NumPy, matplotlib, seaborn)
• Familiarity with one or more cheminformatic or computational toolkits (RDKit, OpenBabel, OpenEye, Schrodinger, etc)
• Experience working in Linux/Unix environments including basic shell scripting and bash commands.
• Experience with cloud platforms (e.g., AWS, Azure), version control systems (e.g., Git), CI/CD pipelines and workload managers (e.g., Slurm)
• Experience designing experiments with scientific data: Prior research involving chemical, biological, or DNA Encoded Library datasets      
• Strong communication and interpersonal skills 

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