Staff Machine Learning Scientist
New
F
Freenome Holdings IncBiotechnology, Cancer Research
This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.Full-TimeStaff
SalaryThe US target range of our base salary for new hires is $199,675 - $283,500.
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Job Details
- Experience
- 6+ years of postdoc or post-PhD industry experience
- Required Skills
- PythonMachine LearningMLFlowPyTorchTensorflowRDeep Learning
Requirements
- PhD or equivalent research experience 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 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, forests, and neural networks.
- 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.
- Proficiency in a general-purpose programming language: Python, R, Java, C, or C++.
- Proficiency in one or more ML frameworks such as PyTorch, Tensorflow, or Jax.
- Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights & Biases.
- Excellent ability to communicate across disciplines and work collaboratively with software engineers and computational biologists.
Responsibilities
- Independently pursue cutting edge research in AI applied to biological problems such as cancer research, genomics, and immunology.
- 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.
- Work closely with ML Engineering partners to ensure that Freenome’s computational infrastructure supports optimal model training and iteration.
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