- Experience
- 3+ yrs
- Location
- United States, Canada, United Kingdom, Ireland, Australia May Also Be Accepted
- Work mode
- Remote
- Job type
- Contract
- Duration
- 12 Weeks
- Weekly
- 10+ hours
- Salary
- $40–$120 / hour
- Posted
- today
About the role
The Life Sciences AI Evaluator – Drug Discovery will apply hands-on drug discovery and preclinical expertise to design realistic scientific tasks, evaluate frontier AI model outputs, and document performance against professional standards. The role requires at least two years of discovery or preclinical experience, an in-progress Bachelor's degree or higher in a relevant field, strong scientific writing, familiarity with end-to-end drug discovery workflows, and the ability to assess work across adjacent scientific disciplines.
Key Responsibilities
- Design challenging, realistic drug discovery tasks based on your own day-to-day workflows, including scenarios, colleague-style prompts, and supporting files.
- Assemble or work with experimental data, protocols, records, reports, correspondence, assay result tables, SAR spreadsheets, DMPK summaries, study reports, and program review decks.
- Run scientific tasks through frontier AI agents and evaluate the resulting workbooks, memos, reports, or slide decks against professional standards.
- Compare outputs from two AI models using identical prompts and files, determine which performed better, and document the strengths and limitations of each response.
- Write detailed grading rubrics defining what correct deliverables must contain, including appropriate compounds advanced, liabilities flagged, and calculations performed.
- Identify and evidence concrete failures such as misread assay data, unsupported SAR conclusions, misinterpreted PK parameters, fabricated or ignored files, unit and scaling errors, missed safety liabilities, and off-brief interpretations.
- Contribute to evaluation tasks spanning target biology, DMPK, preclinical safety, translational and biomarker science, biologics and advanced modalities, CMC, and process development.
- Review and refine tasks created by other subject-matter experts to improve scientific accuracy, realism, and evaluation quality.
- Use real-world scientific judgment to distinguish well-reasoned AI outputs from plausible-sounding but incorrect conclusions.
- Apply feedback, investigate unfamiliar topics independently, and ramp quickly on incomplete or unfamiliar written materials and instructions.
Required Qualifications
- At least 2 years of hands-on experience in a discovery or preclinical setting within pharma, biotech, or an academic drug discovery unit.
- An in-progress Bachelor's degree or higher in Biology or a directly related field, completed in the United States, Canada, Europe, or the UK. Relevant fields include molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, computational biology, chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology.
- Depth of experience in at least one relevant area, such as medicinal chemistry and SAR, lead optimization, computational chemistry and CADD, assay development and screening, DMPK, PK/PD, ADME, preclinical safety and toxicology, translational and biomarker science, protein or antibody engineering, cell and gene therapy, CMC, formulation, or analytical development.
- Working understanding of several adjacent drug discovery areas and the ability to assess whether associated workflows were performed correctly.
- Familiarity with end-to-end drug discovery workflows, from target identification and validation through hit finding, hit to lead, lead optimization, candidate selection, and IND-enabling studies.
- Current or recent hands-on bench or analysis work at an individual-contributor level rather than experience solely in a managerial capacity.
- Ability to draw on real-world experience and day-to-day workflows to create scenarios that test whether an AI system can perform scientific work.
- Full professional or native-level written and spoken English, with strong written communication and the ability to explain complex scientific reasoning clearly and concisely.
- Comfort with ambiguity, strong attention to detail, and the ability to verify document claims against underlying data.
- Ability to interpret feedback, determine which parts are correct, apply it independently, and seek answers when blocked.
- General familiarity with AI and LLM tools, including experience using models such as Claude or ChatGPT in professional life sciences work.
- Baseline technology literacy, including cloud file tools, browser profiles, desktop application installation, spreadsheet conversion, file handling, and file sharing.
- Availability of at least 10 hours per week, with no weekly maximum.
- Based in the United States, Canada, or the UK; Ireland and Australia may also be accepted.
Benefits and Perks
- Fully remote work environment.
- Flexible, self-directed long-form work.
- Opportunity to contribute scientific expertise to the development of better AI systems.
- Minimum availability of 10 hours per week with no weekly maximum; additional hours are welcome.
Skills
- SAR analysis
- Screening cascades
- DMPK
- PK/PD interpretation
- Biomarker strategy
- Candidate selection
- Target validation
- Lead optimization
- Screening triage
- Toxicology package review
- Assay result analysis
- AI model evaluation
- Frontier AI agents
- Grading rubrics
- Experimental data interpretation
- Protocol review
- Medicinal chemistry
- Computational chemistry
- CADD
- Assay development
- Screening
- PK/PD
- ADME
Education
Required
- Bachelor
Selection process
Step 1
Submitted
Someone who fits the role and has told you they are interested, with their resume.
Step 2
Client interviews
Step 3
Selected
If they qualify and are selected, your payout is due.