Machine Learning Engineer
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Senior Machine Learning Scientist

Location: California (Hybrid or Remote Globally)

About the role

We are seeking a Senior Machine Learning Scientist to join our growing Machine Learning Science team. The ideal candidate has deep expertise in artificial intelligence (AI), machine learning (ML), and deep learning (DL), with a proven ability to apply these methods to solve complex research problems.

In this role, you will develop algorithms for early detection of disease using molecular signals in blood. You'll collaborate closely with computational biologists, molecular biologists, and ML engineers to design and execute research experiments. Your work will help advance the field of AI-driven biomedical discovery and have a meaningful impact on patient outcomes.

This role reports to the Director of Machine Learning Science and can be hybrid (2–3 days/week in our Brisbane, CA office) or fully remote (Globally.).

What you'll do

• Pursue cutting-edge research in AI applied to biological problems (e.g., genomics, cancer research, computational biology, immunology).
• Build new models or fine-tune existing models to identify disease-related biological changes.
• Develop models that achieve high accuracy and generalize robustly to new datasets.
• Apply interpretability techniques to uncover biological mechanisms suggested by model findings.
• Work closely with ML Engineering to ensure the computational infrastructure supports optimal model development and deployment.
• Take a transparent, collaborative, and human-centered approach to your research.

Qualifications

Must-haves

• PhD (or equivalent research experience) in a relevant quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering, Computational Biology, Bioinformatics) with an AI/DL emphasis.
• 3+ years of postdoctoral or industry experience delivering impactful ML/DL solutions.
• Expertise in applied machine learning, deep learning, and complex data modeling, demonstrated by publications or industry achievements.
• Strong theoretical and practical understanding of ML and DL techniques, including:
• ML models: generalized linear models, kernel machines, decision trees and forests, neural networks, boosting, Bayesian inference, model selection, and variational inference.
• DL models: large language models and other foundation models.
• Training paradigms: supervised, self-supervised, and contrastive learning.
• Knowledge of state-of-the-art ML/DL approaches and their potential in biological contexts.
• Proficiency in a general-purpose programming language (e.g., Python, R, Java, C++).
• Proficiency in ML frameworks (e.g., PyTorch, TensorFlow, JAX) and platforms (e.g., Hugging Face).
• Familiarity with ML analysis tools (e.g., TensorBoard, MLflow, Weights & Biases).
• Strong communication skills and ability to collaborate across disciplines.
• A passion for innovation and initiative in exploring new research directions.

Nice-to-haves

• Domain expertise in computational biology, genomics, proteomics, or related areas.
• Experience building DL models for genomic or molecular data.
• Familiarity with NGS data analysis and bioinformatics pipelines.
• Experience with containerized cloud environments (e.g., Docker on GCP, Azure, or AWS).
• Experience in a production software engineering environment (including testing, version control, and deployment practices).
• Base salary range: $180,000 – $270,000
• Eligibility for pre-IPO equity, cash bonuses, and comprehensive medical, financial, and wellness benefits.
• Total compensation will depend on factors such as location, experience, skills, and education.

Seniority level

• Seniority levelMid-Senior level

Employment type

• Employment typeFull-time

Job function

• Job functionInformation Technology and Product Management
• IndustriesBiotechnology Research and IT Services and IT Consulting

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Inferred from the description for this job

Medical insurance

Vision insurance

401(k)

Pension plan

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