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Remote Staff Machine Learning Scientist (Remote)grabjobs • United States
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Remote Staff Machine Learning Scientist (Remote)

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  • US
    United States
  • US
    United States
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Ă€ propos

Why join Freenome?
Freenome is a high-growth biotech company developing tests to detect cancer using a standard blood draw. To do this, Freenome uses a multiomics platform that combines tumor and non-tumor signals with machine learning to find cancer in its earliest, most-treatable stages.
Cancer is relentless. This is why Freenome is building the clinical, economic, and operational evidence to drive cancer screening and save lives. Our first screening test is for colorectal cancer (CRC) and advanced adenomas, and it’s just the beginning.
Founded in 2014, Freenome has ~400 employees and continues to grow to match the scope of our ambitions to provide access to better screening and earlier cancer detection.
At Freenome, we aim to impact patients by empowering everyone to prevent, detect, and treat their disease. This, together with our high-performing culture of respect and cross-collaboration, is what motivates us to make every day count.
Become a Freenomer
Do you have what it takes to be a Freenomer? A “Freenomer” is a determined, mission-driven, results-oriented employee fueled by the opportunity to change the landscape of cancer and make a positive impact on patients’ lives. Freenomers bring their diverse experience, expertise, and personal perspective to solve problems and push to achieve what’s possible, one breakthrough at a time.
About this opportunity:
At Freenome, we are seeking a Staff Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, the ability to drive independent research and thrive in a highly cross-functional environment.
They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organization dedicated to changing the entire landscape of cancer.
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.
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
Must haves:
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 haves:
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
Benefits and additional information:
The US target range of our
base salary
for new hires is $199,675 - $302,400.
You will also be eligible to receive pre-IPO equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.
Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page @
freenome.com/job-openings/
for additional company information.
Freenome is proud to be an equal-opportunity employer, and we value diversity. 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.
Applicants have rights under Federal Employment Laws.
Family & Medical Leave Act (FMLA)
Equal Employment Opportunity (EEO)
Employee Polygraph Protection Act (EPPA)
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  • United States

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  • English
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