PhD Pharmacologist or Toxicologist (Drug Discovery), AI Expert (Remote UK or US)

Weekday AI

9h • $70 - $100 • Remote

About The Position

This part-time, fully remote opportunity is for a drug discovery subject-matter expert to help advance AI models for scientific research. The expert will work with AI research and engineering teams to improve model reasoning and accuracy in drug discovery, pharmacology, toxicology, ADME, and PK/PD. Work includes creating training datasets and benchmarks, developing structured solutions to scientific problems, evaluating AI-generated responses, and providing detailed feedback.

The role also involves preparing task specifications, grading rubrics, and verification processes; assessing assay design and quality control; reviewing experimental findings; and collaborating with other subject-matter experts to maintain scientific consistency and rigor. Applicants must be based in the United Kingdom or on the US East Coast. The engagement is as an independent contractor, requires at least 20 hours per week primarily on weekdays, and has a flexible remote schedule. Project duration may vary according to requirements and performance. Compensation is $70–$100 per hour, with weekly payments through Stripe or Wise based on services rendered. The role does not require access to confidential or proprietary information belonging to a current or former employer, client, or institution.

Requirements

• 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 pharmaceutical or biotechnology company, CRO, or academic drug discovery group.
• Hands-on experience analyzing experimental drug discovery data, such as in-vitro ADME and safety assay results, PK/PD studies, CRO-generated assay data and experimental readouts, or 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 and 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.
• Must be based in the United Kingdom or on the US East Coast.
• H-1B and STEM OPT candidates cannot be supported.

Nice To Haves

• 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.

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 domain-specific drug discovery tasks, benchmarks, and training datasets that assess scientific reasoning.
• Develop accurate, structured solutions to complex drug discovery problems, including interpreting assay results, identifying compound liabilities, and planning compound optimization strategies.
• Evaluate AI-generated outputs and step-by-step reasoning for scientific logic, factual accuracy, and appropriate 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 implications for compound selection and optimization.
• Collaborate with other subject-matter experts to ensure consistency, accuracy, and scientific rigor across AI training datasets.

What This Job Offers

• Job Type: Part-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree