Computational Protein Designer
Latent Labs is an active AI-for-science company building generative models and agentic workflows for programmable biology and drug design.
Funding history
About Latent Labs
Latent Labs develops the Latent Labs Platform and the Latent-X model family and Latent-Y agent to help researchers generate protein binders, antibodies, peptides, and other therapeutic molecules.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will design proteins with generative AI, optimize them for experimental validation, and analyze validation results to improve subsequent designs. You will develop biochemical assays, collaborate across machine learning and experimental work, improve model integration and data workflows, and share scientific knowledge.
Requirements
- Experience running end-to-end protein design campaigns
- Knowledge of biochemistry or structural biology
- Experience with generative modeling, including model training and large-scale inference
- PhD or equivalent industry experience in a relevant scientific or technical field
- Research experience in protein biochemistry using computational expertise
- Extensive wet-lab biology experience and ability to execute experimental validation
- Track record of successful commercial or academic research projects
- Communication and presentation skills
- Experience with lab automation
Responsibilities
- Analyze protein design problems using functional requirements, biochemistry, structural biology, and sequence homology
- Generate and optimize protein designs using generative AI models
- Coordinate protein design and validation strategies with lab-based protein engineers
- Use experimental results to improve subsequent design rounds
- Collaborate with machine learning scientists to fine-tune and prompt models
- Develop and execute experimental validation strategies, including protein expression, purification, biophysical characterization, and functional assays
- Communicate bioengineering learnings between machine learning development and experimental validation
- Improve AI model integration, data management systems, and workflows
- Maintain publication-grade scientific standards and stay current with synthetic biology developments
- Organize and present at internal reading groups and attend relevant conferences
Benefits
- Private health insurance
- Pension/401(K) contributions
- Generous leave policies, including gender-neutral parental leave
- Hybrid working
- Travel opportunities