The successful candidate will be expected to:

• Help design and contribute to the alpha research platform
• Support, maintain, and test their own code following best practices, including unit testing, regression testing, documentation, and automation within typical CI processes
• Provide leadership and vision to help determine the overall direction, design, and architecture of the alpha research pipeline
• Mentor junior resources
• Regularly interact with quantitative researchers and other stakeholders, and prioritise and implement features

The ideal candidate will have:

• 5+ years of Python experience in a quantitative finance setting
• Familiarity with linear models and basic statistics for creating model evaluation and reporting workflows
• Familiarity with the Python data science ecosystem, including dashboarding and popular ML libraries such as Plotly, Altair, JAX, TensorFlow, and PyTorch
• Prior experience building alpha research or machine learning pipelines
• Highly analytical with strong problem-solving skills and attention to detail
• Strong communication skills, with the ability to explain technical and sophisticated concepts clearly and concisely
• Ability to tune and debug runtime performance of data applications
• Familiarity with C++/Rust/CUDA to debug and profile underlying native code in ML libraries (Nice to have)

The estimated base salary range for this position is $160,000 to $250,000, which is specific to New York and may change in the future.

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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.

Key responsibilities include:

• Independently pursuing cutting-edge research in AI applied to biological problems
• Building new models or fine-tuning existing models to identify biological changes resulting from disease
• Building models that achieve high accuracy and that generalise robustly to new data
• Applying contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms
• Working closely with ML Engineering partners to ensure that Freenome's computational infrastructure supports optimal model training and iteration
• Taking a mindful, transparent, and humane approach to your work

Requirements include:

• 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
• 3+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modelling techniques
• Expertise, demonstrated by research publications or industry achievements, in applied machine learning, deep learning and complex data modelling
• Practical and theoretical understanding of fundamental ML models like generalised linear models, kernel machines, decision trees and forests, 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
• 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
• A passion for innovation and demonstrated initiative in tackling new areas of research

Nice to have qualifications include:

• 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

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