Staff Machine Learning Scientist
Freenome · Remote
About this role
**About this opportunity** At Freenome, we’re seeking a **Staff Machine Learning Scientist** to help grow the Machine Learning Science team within the Computational Science department. The ideal candidate has strong AI/ML fundamentals, extensive deep learning experience, and a track record of using these methods to answer complex research questions. You’ll drive independent research and thrive in a highly cross-functional environment. In this role, you’ll develop algorithms for **early, blood-based cancer detection tests**. You’ll build on ML/DL and statistical skills to develop models that identify **molecular signals from blood**, and collaborate with computational biologists, molecular biologists, and ML engineers to design and drive research experiments—making a significant impact on an organization dedicated to changing the landscape of cancer. **Reporting line:** Director, Machine Learning Science **Location / work model:** Hybrid (Brisbane, California; **2–3 days/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 generalize robustly to new data - Apply contemporary interpretability techniques to better understand the underlying signals identified by the model (ideally suggesting potential biological mechanisms) - Work closely with ML Engineering partners to ensure Freenome’s 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 in a relevant quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering, Computational Biology, Bioinformatics) - **6+ years** of postdoc or post-PhD industry experience delivering impactful results using relevant modeling techniques - Demonstrated expertise via research publications or industry achievements, including independent research in applied machine learning, deep learning, and complex data modeling - Practical and theoretical understanding of core ML models (e.g., generalized linear models, kernel machines, decision trees/forests, neural networks, boosting, model aggregation) - Practical and theoretical understanding of DL models (e.g., large language models or other foundation models) - Extensive experience with training paradigms such as supervised learning, self-supervised learning, and contrastive learning - Proficiency in current state-of-the-art ML/DL approaches across domains, with the ability to envision applications in biological data - Proficiency in a general-purpose programming language (e.g., Python, R, Java, C/C++) - Proficiency in one or more ML frameworks (e.g., PyTorch, TensorFlow, JAX) and ML platforms (e.g., Hugging Face) - Experience with ML analysis and developer tools (e.g., TensorBoard, MLflow, Weights & Biases) - Excellent ability to communicate across disciplines, collaborate, and make progress via experimental iteration - Strong cross-functional scientific communication with software engineers and computational biologists - Passion for innovation and demonstrated initiative in tackling new research areas --- **Nice to haves** - Deep domain-specific experience in computational bi
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