Freenome is seeking a Staff Machine Learning Scientist to join the Machine Learning Science team within the Computational Science department. The individual will lead cutting-edge AI research applied to biological problems, particularly in cancer detection using blood-based biomarkers, and will collaborate across disciplines to develop robust, interpretable models.
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
What We're Looking For
- 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 Have
- 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
Technical Stack
- Python
- R
- Java
- C
- C++
- Pytorch
- Tensorflow
- Jax
- Hugging Face
- TensorBoard
- MLflow
- Weights & Biases
- Docker
- GCP
- Azure
- AWS
Team & Environment
- Machine Learning Science team within the Computational Science department
- Reports to Director, Machine Learning Science
Benefits & Compensation
- Equity
- Cash bonuses
- Full range of medical, financial, and other benefits
- Family & Medical Leave Act (FMLA)
- Equal Employment Opportunity (EEO)
- Employee Polygraph Protection Act (EPPA)
US target range of base salary: $199,675.00 - $283,500.00. Eligible to receive equity.
Work Mode
- Hybrid role with option to work remotely or partially onsite
- Brisbane, California (2-3 days per week in office)
- Remote
Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.