Staff Machine Learning Scientist

About this opportunity:

At Freenome, we are seeking a Staff Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department. 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.

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. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.

What you’ll do:

• 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.
• Must haves: 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.
• Nice to haves: Deep domain-specific experience in computational biology, genomics, proteomics or a related field.
• Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models.
• Experience in NGS data analysis and bioinformatic pipelines.
• Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS.
• Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems.