AI Biologist - Biologics Development
New
L
LatchBiologics development
Remote (anywhere)Contract
Salary120,000 - 180,000 USD per year
Apply NowOpens the employer's application page
Job Details
- Required Skills
- PythonR
Requirements
- PhD in protein engineering, biochemistry, analytical chemistry, pharmacology, or a related field.
- Hands-on experience analyzing antibody development data.
- Deep expertise in at least one of analytical/CMC characterization, large-molecule DMPK, protein engineering and developability, or non-clinical safety.
- Working fluency across the other listed biologics development areas.
- Command of how readouts map to critical quality attributes, PK and effector properties, and release or comparability decisions.
- Python or R for data analysis, including parsing chromatograms and electropherograms, kinetic fitting, or PK modeling.
- Daily use of Claude, Cursor, or similar AI agents, with the judgment to identify scientifically unsound model reasoning.
- Ability to overrule an AI agent when evidence demands it and update a conclusion when shown wrong.
- Ability to calibrate confidence and communicate uncertainty.
- Bonus: antibody format and Fc engineering experience, including IgG subclasses, YTE/LS, effector-silencing, or bispecific architectures.
- Bonus: familiarity with MAM, ICH Q6B/Q5E, FDA immunogenicity guidance, IMGT, SAbDab, or IEDB.
Responsibilities
- Turn biologics development workflows into file-based tasks with structured outputs and deterministic grading.
- Write gold-standard answers from raw data, accounting for replicate, integration, and fitting variability.
- Curate fixtures covering developability, analytical characterization, PK, immunogenicity, and effector assays.
- Partner with software engineers to develop benchmark workflows.
- Test whether agents distinguish true liabilities from analytical artifacts and assess evidence across readouts.
- Evaluate whether agents connect effector function and affinity to mechanism and distinguish clearance types.
- Interrogate agent reasoning and challenge conclusions that would not withstand a development review or go/no-go decision.
- Identify why agent reasoning fails, what an expert would do instead, and what evidence could change the assessment.
- Score agent confidence, rewarding explicit uncertainty over overconfident calls.
View Full Description & ApplyYou'll be redirected to the employer's site
