AI Research Scientist: Biomedical ML & Large-Scale AI

Jobtailor; Seattle (WA); On-site; USD 150,000 - 210,000; Full time

Job summary

Jobtailor in Seattle is seeking an experienced AI/ML researcher to develop, train, and evaluate large-scale models across diverse biological data types. You will translate complex biological questions into computational experiments and benchmarks, and design scalable experiments for rigorous model analysis.

The role requires a PhD or equivalent in a relevant field, strong Python skills, and experience with PyTorch or JAX.

Qualifications

• PhD in CS, math, statistics, computational biology, or related field.
• Equivalent combination of degree and experience considered.
• Experience developing novel AI/ML approaches.

Responsibilities

• Develop, train, and evaluate large-scale AI/ML models across diverse biological data types.
• Investigate new modeling paradigms to address open biological research questions.
• Translate complex scientific questions into computational frameworks, experiments, and benchmarks.
• Assess model behavior and interpretability in scientific contexts.
• Design and run experiments at scale with benchmarking and model analysis.
• Build reusable modeling frameworks and document methods for reproducibility.

Skills

Python

Deep learning

ML frameworks

Research

Education

PhD in relevant field

Tools

PyTorch

JAX

Job description

• Develop, train, and evaluate large-scale AI/ML models across diverse biological data types.
• Investigate new modeling paradigms to address open biological research questions.
• Translate complex scientific questions into computational frameworks, experiments, and benchmarks.
• Assess model behavior and interpretability in scientific contexts.
• Design and run experiments at scale with benchmarking and model analysis.
• Build reusable modeling frameworks and document methods for reproducibility.

Requirements

• PhD in Computer Science, Applied Mathematics, Statistics, Computational Biology, or a related field; or equivalent combination of degree and experience.
• Demonstrated experience developing and evaluating novel AI/ML approaches for complex, large-scale datasets.
• Proficiency in Python and modern deep learning frameworks (e.g., PyTorch, JAX).
• Strong foundation in machine learning, deep learning, and scientific computing, including experimentation, benchmarking, and model analysis.
• Research experience applying machine learning to biological, genomic, or other life-science data is preferred.
• Familiarity with large-scale training and evaluation environments and scientific computing libraries.

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Hard Skills

• AI/ML Approaches
• Model Evaluation
• Experimentation
• Benchmarking
• Model Analysis