Remote AI Biologist for Rare Disease Data & AI Research

RiseMe; San Francisco (CA); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

2x daily meals

Waterfront office near Oracle Park

Monthly parking

Team outings and offsites

Job summary

Latch is seeking an AI Biologist to join the Rare Disease team, exploring how AI can aid in diagnostics and research for rare diseases. You will work with genomic, phenotypic, and clinical data, building benchmarks and workflows that translate evidence into patient-centric decisions.

The role emphasizes collaboration with engineers and cross-disciplinary teams, aiming to advance AI-enabled biology in a fast-paced environment.

Qualifications

• Master's, PhD, or equivalent in biology or quantitative field.
• 3+ years hands-on experience in basic or translational research, clinical genomics, rare disease diagnostics, or rare disease therapeutics.
• Deep expertise with therapeutics discovery, biological mechanism, or clinical practice in a subset of rare diseases.
• Experience interpreting data in biological context and identifying confounders, artifacts, and limitations of experiments.
• Proficiency in Python/R for biological data analysis or visualization.

Responsibilities

• Structure, curate, and gain insights from extensive genomic, phenotypic, pedigree, transcriptomic, and longitudinal clinical data.
• Navigate data partnerships.
• Evaluate realistic patient case scenarios spanning the diagnostic journey.
• Partner with software engineers to define and execute new data workflows and benchmarks.

Skills

Python

R

Genomics

Biology

Data interpretation

Education

Master's/PhD in biology or quantitative field

Job description

Latch is seeking an AI Biologist to join the Rare Disease team, exploring how AI can aid in diagnostics and research for rare diseases. You will work with genomic, phenotypic, and clinical data, building benchmarks and workflows that translate evidence into patient-centric decisions.

The role emphasizes collaboration with engineers and cross-disciplinary teams, aiming to advance AI-enabled biology in a fast-paced environment.