Latchbio
AI Biologist - Aging & Longevity
San Francisco (preferred), Remote. Posted 4 days ago
The role
You will use scientific expertise to improve the ability of AI agents to reason like expert geroscientists. You will build tasks to evaluate aging-specific analysis capabilities and scientific taste.
What you will do
- Develop, apply, and interpret biomarkers of aging
- Design and analyze experiments related to the Hallmarks of Aging
- Integrate and analyze diverse data modalities
- Use high-throughput screening data to discover and prioritize candidate anti-aging drug targets
- Build benchmarks testing capabilities from geroscience research to therapeutic discovery
Required qualifications
- Advanced degree (MS/PhD) in biology, bioinformatics, computational biology, or a related field
- Contribution to published research in aging biology or an adjacent field
- Deep familiarity with AI coding tools
- Strong scientific writing skills
- High agency and initiative
- Demonstrated track record of using R or Python to analyze scientific data
- Computational experience applying machine learning, data analysis, or statistics to -omics data, methods development, or statistical genetics
- Experimental experience performing experiments and assays relevant to aging research
Nice to have
- Industry experience in biotech, tech, or a similar field
- Multiple first-author publications
- Familiarity with frequently used repositories of scientific data, such as: the UK Biobank, TCGA, GEO, ENCODE, etc.
Benefits
- Health, dental, vision: 100% covered for you, 50% for dependents
- 401(k) (no match)
- PTO: 12 days to start
- Catered lunch and dinner (onsite)
- Unlimited snacks (onsite)
- Company outings most months
- Team off-sites
Before you apply
- If your profile matches, we’ll send you a take-home project
- Background check, once you accept
- We sponsor H-1B (new and transfers), O-1, and TN, and support STEM OPT
About the company
Latch builds rigorous scientific benchmarks for AI agents across biology. They work with frontier labs and pharma to measure whether agents can handle real biological workflows.