Staff Machine Learning Scientist, Freenome

• The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, the ability to drive independent research and thrive in a highly cross-functional environment
• PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics
• 6+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modeling techniques
• Expertise demonstrated by research publications or industry achievements, in driving independent research in applied machine learning, deep learning and complex data modeling
• Practical and theoretical understanding of fundamental ML models like generalized linear models, kernel machines, decision trees and forests, neural networks, boosting and model aggregation
• Practical and theoretical understanding of DL models like large language models or other foundation models
• Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning
• Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data
• Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc
• Proficiency in one or more ML frameworks such as; Pytorch, Tensorflow and Jax; and ML platforms like Hugging Face
• Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights & Biases
• Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations
• Proficient at productive cross-functional scientific communication and collaboration with software engineers and computational biologists
• A passion for innovation and demonstrated initiative in tackling new areas of research

Desirable

• At Freenome, we are seeking a Staff Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department
• They will be responsible for the development of algorithms for early, blood-based detection tests for cancer
• They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood
• They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organization dedicated to changing the entire landscape of cancer
• The role reports to the Director, Machine Learning Science
• Independently pursue cutting edge research in AI applied to biological problems (including cancer research, genomics, computational biology, immunology, etc.)
• Build new models or fine-tune existing models to identify biological changes resulting from disease
• Build models that achieve high accuracy and that generalize robustly to new data
• Apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms
• Work closely with ML Engineering partners to ensure that Freenome’s computational infrastructure supports optimal model training and iteration
• Take a mindful, transparent, and humane approach to your work