Machine Learning Scientist

About The Company

Freenome is a pioneering biotechnology company dedicated to transforming cancer detection and treatment through innovative blood-based diagnostics. Leveraging cutting-edge artificial intelligence, machine learning, and genomic technologies, Freenome aims to detect cancer at its earliest stages, significantly improving patient outcomes. The organization fosters a collaborative and inclusive environment, encouraging scientific excellence and technological innovation to address some of the most pressing challenges in healthcare today. With a commitment to advancing personalized medicine, Freenome is at the forefront of developing scalable, non-invasive testing solutions that can be integrated into routine clinical practice worldwide.

About The Role

We are seeking a highly skilled Staff Machine Learning Scientist to join our Machine Learning Science team within the Computational Science department. This role offers an exciting opportunity to lead innovative research and development efforts focused on early cancer detection. The ideal candidate will possess a deep understanding of artificial intelligence, including machine learning and deep learning methodologies, and have a proven track record of applying these techniques to complex biological data. You will be responsible for developing sophisticated algorithms to identify molecular signals from blood samples, collaborating closely with cross-disciplinary teams such as computational biologists, molecular biologists, and ML engineers. Your work will directly impact the development of novel diagnostic tests, contributing to Freenome’s mission of changing the landscape of cancer diagnosis and treatment.

This position reports to the Director of Machine Learning Science. It offers flexibility with a hybrid work model based in our Brisbane, California headquarters, requiring 2-3 days per week in the office, or the option to work remotely depending on candidate preference and organizational needs.

Qualifications

• PhD or equivalent research experience in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics.
• Minimum of 6+ years of postdoctoral or industry experience demonstrating impactful results using advanced modeling techniques.
• Proven expertise through research publications or industry achievements in applied machine learning, deep learning, and complex data modeling.
• Strong understanding of fundamental ML models including generalized linear models, kernel methods, decision trees, neural networks, boosting, and model aggregation.
• In-depth knowledge of deep learning models such as large language models and foundation models.
• Experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning.
• Proficiency in current state-of-the-art ML/DL approaches and their applications to biological data.
• Proficiency in programming languages such as Python, R, Java, C, or C++.
• Experience with ML frameworks like PyTorch, TensorFlow, Jax, and platforms such as Hugging Face.
• Familiarity with ML analysis and developer tools including TensorBoard, MLflow, or Weights & Biases.
• Excellent communication skills across disciplines and ability to collaborate effectively with software engineers and biologists.
• A demonstrated passion for innovation and independent research initiative.

Responsibilities

• Conduct independent, cutting-edge research applying AI to biological problems, including cancer genomics, immunology, and computational biology.
• Develop and refine machine learning models to identify biological signals indicative of disease states from blood-based data.
• Build models with high accuracy and robustness, ensuring their ability to generalize across diverse datasets.
• Implement interpretability techniques to elucidate the biological mechanisms underlying model predictions.
• Collaborate with ML engineering teams to optimize computational infrastructure supporting model training and deployment.
• Drive experimental design and research initiatives to advance Freenome’s diagnostic capabilities.
• Document research findings and communicate insights effectively across technical and non-technical teams.
• Stay current with advancements in AI, genomics, and related fields, integrating new techniques into ongoing projects.

Benefits

• Competitive salary range, with the US target range of $199,675 - $283,500.
• Eligibility for equity, cash bonuses, and comprehensive medical, dental, and vision insurance.
• Financial and retirement planning benefits.
• Opportunities for professional development and continuous learning.
• Collaborative and inclusive work environment fostering innovation.
• Flexible work arrangements including hybrid or remote options.

Equal Opportunity

Freenome is proud to be an equal-opportunity employer. We value diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on 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 protected status under federal, state, or local law. All qualified applicants will receive consideration for employment without regard to these factors. Applicants have rights under Federal Employment Laws.