[Hiring] PhD Pharmacologist or Toxicologist (Drug Discovery), AI Expert @Weekday AI

Role Description

We are seeking highly qualified PhD-level Pharmacologists and Toxicologists with hands-on experience in drug discovery and development to contribute to the advancement of next-generation AI models for scientific research. This opportunity is ideal for professionals specializing in pharmacology, toxicology, drug metabolism and pharmacokinetics (DMPK), or ADME sciences, with experience in pharmaceutical, biotechnology, contract research, or academic drug discovery environments.

As a Drug Discovery Subject-Matter Expert (SME), you will collaborate with AI research and engineering teams at a leading AI lab to improve the scientific reasoning, accuracy, and problem-solving capabilities of advanced AI models. Your expertise will help develop high-quality training datasets, design challenging scientific benchmarks, and evaluate AI-generated solutions across mechanism of action, target pharmacology, ADME/Tox, and PK/PD.

This is a part-time, fully remote opportunity for candidates based in the United Kingdom or on the US East Coast.

Key Responsibilities

• Work with AI research and engineering teams to identify scientific knowledge gaps and improve model performance across drug discovery, pharmacology, toxicology, ADME, and PK/PD.
• Design challenging, domain-specific drug discovery tasks, benchmarks, and training datasets that assess genuine scientific reasoning.
• Develop accurate, structured solutions to complex drug discovery problems, including interpretation of assay results, identification of compound liabilities, and planning of compound optimization strategies.
• Evaluate AI-generated outputs and step-by-step reasoning, identifying errors in scientific logic, factual accuracy, and the use of scientific tools.
• Provide clear, detailed written feedback to improve the reliability and scientific quality of AI-generated responses.
• Develop task specifications, grading rubrics, and verification processes for drug discovery data produced by external technical teams.
• Assess assay design and quality control, identify common experimental failure modes, and validate the biological and pharmacological relevance of AI-generated recommendations.
• Review experimental findings and scientific evidence to determine their implications for compound selection and optimization.
• Collaborate with other subject-matter experts to ensure consistency, accuracy, and scientific rigor across AI training datasets.

Qualifications

• PhD in Pharmacology, Toxicology, Pharmaceutical Sciences, DMPK, Biochemistry, or a closely related discipline. Equivalent research experience may also be considered.
• At least 3 years of experience in early- to mid-stage drug discovery, pharmacology, DMPK/ADME, or toxicology within a recognized pharmaceutical or biotechnology company, contract research organization (CRO), or academic drug discovery group.
• Hands-on experience analyzing experimental drug discovery data, including one or more of the following:
  • In-vitro ADME and safety assay results.
  • Pharmacokinetic and pharmacodynamic (PK/PD) studies.
  • CRO-generated assay data and experimental readouts.
  • LC-MS-based bioanalytical data.
• Ability to interpret experimental findings and use scientific evidence to guide compound selection, safety assessment, and optimization decisions.
• Familiarity with PK/PD or data analysis software and scientific AI tools. Programming expertise is not required.
• Strong analytical and problem-solving abilities, with an understanding of drug discovery workflows and experimental methodologies.
• Excellent written communication skills, including the ability to explain complex scientific concepts and technical decisions clearly.
• Ability to commit reliably to at least 20 hours per week during weekdays.

Preferred Qualifications

• Experience with PK/PD modeling, ADME modeling, or quantitative pharmacology.
• Familiarity with drug discovery data interpretation, compound profiling, and toxicity assessment.
• Experience developing scientific benchmarks, evaluation tasks, grading rubrics, or structured assessment frameworks.
• Previous experience evaluating AI models or AI agents for scientific accuracy and reasoning.
• Ability to translate complex experimental workflows and scientific findings into clear, structured technical documentation.

Employment Details

• Work Arrangement: Fully remote.
• Eligible Locations: United Kingdom or US East Coast.
• Engagement Type: Part-time, independent contractor.
• Time Commitment: Minimum 20 hours per week, primarily on weekdays.
• Schedule: Flexible, with the ability to manage work remotely.
• Project Duration: Engagements may be extended, shortened, or concluded early depending on project requirements and performance.
• Payment: Weekly payments through Stripe or Wise, based on services rendered.
• Confidentiality: The role will not require access to confidential or proprietary information belonging to any current or former employer, client, or institution.

Equal Employment Opportunity

We are committed to providing equal employment opportunities to all qualified applicants. We do not discriminate based on race, religion, color, national origin, sex, pregnancy, sexual orientation, gender identity or expression, age, disability, veteran status, genetic information, political views or activity, or any other characteristic protected by applicable law. Reasonable accommodations are available to qualified applicants upon request.

Important Eligibility Information

• Applicants must be based in the United Kingdom or on the US East Coast.
• H-1B and STEM OPT candidates cannot be supported for this opportunity.
• Candidates should have a PhD or equivalent research experience, along with substantial practical expertise in pharmacology, toxicology, or drug discovery.