Postdoctoral Researcher

Labela; United States; On-site; USD 227,000 - 324,000; Full time

Benefits offered by this job

Remote work

Flexible hours

Rolling applications

Job summary

Labela is hiring a Drug Discovery Expert to design and grade tasks that test AI models across full discovery campaigns from target validation to IND-enabling work.

You will write multi-step drug discovery tasks, grade model outputs against bench expectations, and build robust scoring rubrics. Remote contract with flexible hours, rolling review, and hourly compensation.

Qualifications

• PhD or equivalent depth in a relevant field.
• Experience carrying a program across more than one discovery stage.
• Hands-on work in medicinal chemistry, SAR, structure-based or computational drug design, HTS and assay development, translational pharmacology and PK/PD, preclinical and IND-enabling development.
• Ability to explain why a decision was made, not only what was decided; reasoning should be legible.

Responsibilities

• Write multi-step drug discovery tasks spanning target validation through hit identification, lead optimization, and IND-enabling work.
• Grade model outputs against bench reality and explain why an answer fails.
• Build scoring rubrics that hold up under different expert applications.
• Flag where a model is confidently wrong in ways that could waste a year of program time.
• Turn recurring failure patterns into new task sets with the research team.

Education

PhD in medicinal chemistry, chemical biology, pharmacology, computational chemistry, or related discipline

Job description

Drug Discovery Expert, Long-Horizon AI Tasks

Labela builds the evaluation data that frontier AI models are measured against in healthcare and life sciences. We're hiring drug discovery scientists to design and grade tasks that test whether a model can reason across a full discovery campaign, not answer a single question about it.

Most AI evaluation in this space stops at textbook recall. A model can define a PROTAC. It cannot yet decide which of four hit series to kill at week 30, or spot that a DMPK profile is going to sink a program before anyone has run the study. That gap is what you'd work on.

What you'll do

• Write multi-step drug discovery tasks that span real campaign timelines: target validation through hit identification, hit to lead, lead optimization, candidate selection, IND-enabling work
• Grade model outputs against what would actually happen at the bench, and say precisely why an answer fails
• Build scoring rubrics that hold up when a different expert applies them
• Flag where a model is confidently wrong in ways that would waste a year of program time
• Turn recurring failure patterns into new task sets with our research team

What we're looking for

• PhD in medicinal chemistry, chemical biology, pharmacology, computational chemistry, or a related discipline, or equivalent industry depth
• Direct experience carrying a program across more than one discovery stage. We care much more about one project you followed from target to candidate than about breadth across many short engagements
• Hands-on work in at least one of: medicinal chemistry and SAR, targeted protein degradation, structure-based or computational drug design, HTS and assay development, translational pharmacology and PK/PD, preclinical and IND-enabling development
• Ability to explain why a decision was made, not only what was decided. Most of the value here is in your reasoning being legible

Helpful, not required

• Industry experience at a pharma, biotech, or CRO
• Patents, or publications where you were the driving author
• Prior work training or evaluating AI models
• Experience managing CRO relationships or cross-functional program decisions

Details

• $200 per hour
• Contract, remote, flexible hours around your existing work
• No weekly minimum hours required, work around your schedule, from anywhere.
• Applications reviewed on a rolling basis