Freenome

5 days ago

Staff Machine Learning Scientist

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 11 hours agoStatus: Live

Job Summary

Develop AI/ML algorithms for early, blood-based cancer detection, building on ML/DL and statistical methods to identify molecular signals from blood. Conduct independent, cutting-edge research applied to biological problems (cancer, genomics, computational biology, immunology), improving model accuracy and generalization. Use interpretability techniques to surface underlying biological mechanisms and partner with ML Engineering to support training and iteration infrastructure in a cross-functional environment.

Location: Brisbane

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Independently pursue cutting-edge AI research applied to biological problems, including cancer research, genomics, computational biology, and immunology.
•Build or fine-tune ML/DL models to identify biological changes resulting from disease and molecular signals from blood.
•Develop models that achieve high accuracy and robust generalization to new data.
•Apply interpretability techniques to understand underlying model signals and suggest potential biological mechanisms.
•Partner with ML Engineering to ensure computational infrastructure supports optimal model training and iteration.

Pay and Benefits

Salary: USD 199,675 - 283,500

Perks:Equity

Key Requirements

•PhD or equivalent research experience with an AI emphasis 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 delivering impactful results using relevant modeling techniques.
•Demonstrated independent research impact through publications or industry achievements in applied machine learning, deep learning, and complex data modeling.
•Strong practical/theoretical understanding of fundamental ML models (e.g., generalized linear models, kernel machines, decision trees/forests, neural networks, boosting, model aggregation).
•Strong practical/theoretical understanding of DL models (including large language models/foundation models) and experience with supervised, self-supervised, and contrastive learning.

Experience:6+ yearsComputational biologyGenomicsBioinformatics

Education:PhD / Doctorate

Skills:Cross-functional collaborationCommunicationIndependent researchInnovationIterative experimentation

Languages:English

Tech Stack:PythonRJavaCC++PyTorchTensorFlowJAXHugging FaceTensorBoardMLflowWeights & BiasesGeneralized linear modelsKernel machinesDecision treesRandom forestsNeural networksBoostingModel aggregationLarge language models

Company Brief

Freenome

Develops blood-based early cancer detection tests by combining cell-free DNA analysis, genomics, and machine learning to identify cancer signals for screening and diagnosis, aiming to enable earlier, noninvasive detection across multiple cancer types.

Industry: Diagnostics

Company Size: Large (251 to 1,000 employees)

Growth: Growth Stage Startup

Headquarters: South San Francisco, United States

Founded: 2014

Website LinkedIn

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Freenome

5 days ago

Staff Machine Learning Scientist

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 11 hours agoStatus: Live

Job Summary

Location: Brisbane

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Independently pursue cutting-edge AI research applied to biological problems, including cancer research, genomics, computational biology, and immunology.
•Build or fine-tune ML/DL models to identify biological changes resulting from disease and molecular signals from blood.
•Develop models that achieve high accuracy and robust generalization to new data.
•Apply interpretability techniques to understand underlying model signals and suggest potential biological mechanisms.
•Partner with ML Engineering to ensure computational infrastructure supports optimal model training and iteration.

Pay and Benefits

Salary: USD 199,675 - 283,500

Perks:Equity

Key Requirements

•PhD or equivalent research experience with an AI emphasis 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 delivering impactful results using relevant modeling techniques.
•Demonstrated independent research impact through publications or industry achievements in applied machine learning, deep learning, and complex data modeling.
•Strong practical/theoretical understanding of fundamental ML models (e.g., generalized linear models, kernel machines, decision trees/forests, neural networks, boosting, model aggregation).
•Strong practical/theoretical understanding of DL models (including large language models/foundation models) and experience with supervised, self-supervised, and contrastive learning.

Experience:6+ yearsComputational biologyGenomicsBioinformatics

Education:PhD / Doctorate

Skills:Cross-functional collaborationCommunicationIndependent researchInnovationIterative experimentation

Languages:English

Company Brief

Freenome

Industry: Diagnostics

Company Size: Large (251 to 1,000 employees)

Growth: Growth Stage Startup

Headquarters: South San Francisco, United States

Founded: 2014

Website LinkedIn