Research Scientist
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 join a team at the interface of generative AI and biology, designing, executing, optimizing and troubleshooting high-throughput cell-based and biochemical assays to evaluate model-generated proteins. You will work with technical expert consultants to establish new experimental capabilities, collaborate with the automation team to improve screening workflows, analyze data in Python, document work in Benchling or a LIMS, and work with ML colleagues to turn results into insights for subsequent design cycles. The Sr. Scientist level also involves mentoring and managing complex projects with CROs.
Requirements
- PhD in molecular biology, cell biology, biochemistry, or a related field
- 1-3 years of relevant industry research experience for Scientist; more than 3 years for Sr. Scientist
- Experience taking an assay from feasibility testing through optimization and reproducible execution
- Experience with high-throughput workflows, ideally plate-based assays and laboratory automation
- Experience in molecular biology and mammalian cell culture
- Proficiency in Python and Jupyter notebooks
- Experience with Benchling, a LIMS, or comparable tools
- Depth in at least one of: gene editing and protein–DNA interaction characterization, membrane protein characterization, protein biochemistry and biologics characterization
- Sr. Scientist: experience mentoring others and managing complex projects with CROs, including SOWs and contract negotiation
Responsibilities
- Independently design, execute, optimize, and troubleshoot cell-based and biochemical assays for model-generated proteins
- Select and implement new assays with technical expert consultants and translate specialist knowledge into reproducible internal workflows
- Collaborate with the automation team to increase throughput, reproducibility, and efficiency of screening workflows
- Process experimental data in Python, assess assay quality, and communicate findings and limitations
- Establish appropriate controls and QC criteria
- Document methods, samples, conditions, and results using Benchling, a LIMS, or similar tools
- Work with ML colleagues to provide documented data and biological context and translate results into actionable insights
Benefits
- Competitive compensation
- Equity
- Benefits