Pharmaceutical R&D Domain Expert

Weekday AI•California City, CA

3d•Hybrid

About The Position

This role is for one of our clients. We are seeking a senior pharmaceutical R&D domain expert to work with a leading AI research team on improving how advanced AI models understand and reason about real-world pharmaceutical research and development. In this role, your expertise in drug discovery and development will be central to defining what high-quality pharmaceutical knowledge work looks like. You will evaluate AI-generated outputs, develop detailed instruction specifications, create reference-quality solutions, and design benchmarks that measure the accuracy, depth, and practical relevance of AI systems. The ideal candidate is a practicing or experienced pharmaceutical R&D specialist with substantial industry experience and deep expertise in a specific area of the drug development lifecycle.

Requirements

PhD or PharmD in a relevant pharmaceutical or life sciences discipline, such as: Medicinal or Organic Chemistry, Pharmacology, Molecular Biology, Pharmaceutical Sciences, Toxicology. MD candidates with substantial pharmaceutical drug development experience may also be considered.
4+ years of substantive pharmaceutical or biotechnology research and development experience.
Experience within a pharmaceutical company, biotechnology company, contract research organization, or translational research institute is strongly preferred.
Demonstrated specialization in at least one major area of the pharmaceutical development pipeline, such as: Target identification and validation, Medicinal chemistry and lead optimization, Preclinical pharmacology, Toxicology, Translational science and biomarkers, Clinical development, Regulatory affairs, Chemistry, Manufacturing and Controls (CMC).
Demonstrated progression into senior-level roles such as Senior Scientist, Principal Scientist, Director of Research, or VP-level R&D positions.
Experience owning or making significant contributions to a discovery project or pharmaceutical development program.
Practical experience using large language models or AI tools in a professional setting.
Strong ability to distinguish scientifically rigorous reasoning from responses that merely appear credible.
Excellent analytical judgment and attention to scientific detail.
Excellent written and verbal communication skills.
Ability to provide precise, structured, and actionable written feedback.
Comfortable translating complex scientific concepts into clear evaluation criteria.
Able to commit approximately 40 hours per week for an initial six-month engagement.
Currently based in the Bay Area, California, with the ability to work on-site multiple days per week as required.
Candidates outside the Bay Area must be willing to relocate at their own expense before the engagement begins.

Nice To Haves

Candidates with purely academic research backgrounds and limited exposure to commercial drug development may be less suitable.
Peer-reviewed publications, granted patents, or meaningful contributions to pharmaceutical programs that progressed into clinical development are strongly preferred.

Responsibilities

Review pharmaceutical knowledge tasks and AI-generated outputs for scientific accuracy, completeness, and practical relevance.
Identify unsupported efficacy or safety claims, gaps in reasoning, missing considerations, and conclusions that would not withstand scientific or regulatory scrutiny.
Assess whether AI responses reflect genuine pharmaceutical R&D expertise rather than surface-level or plausible-sounding reasoning.
Develop detailed instruction specifications that accurately reflect real-world pharmaceutical research and development workflows.
Create high-quality reference solutions for drug discovery and development problems.
Design new tasks that capture the complexity, judgment, and decision-making involved in industry pharmaceutical R&D.
Develop challenging pharmaceutical evaluation tasks and domain-specific benchmark datasets.
Help establish evaluation standards for measuring AI performance across different stages of the pharmaceutical pipeline.
Collaborate with research teams to identify opportunities for improving domain-specific AI capabilities.
Work with researchers and specialists from adjacent disciplines to establish consistent evaluation standards.
Translate complex, experience-based pharmaceutical judgment into clear, objective, and teachable evaluation criteria.
Provide precise written feedback that helps improve both AI outputs and evaluation methodologies.