Remote Preclinical ADC & Bispecifics Scientist

mercor

United States

Remote

USD 117,000 - 196,000

Part time

14 days+
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Job summary

Mercor connects elite creative and technical talent with leading AI research labs. Mercor currently seeks a Preclinical R&D Expert focusing on ADC and bispecifics for remote, hourly engagement.

The contractor will review complex R&D data, annotate model outputs, and assist in critical development decisions across discovery to preclinical stages. You will work independently in a fast-paced environment, contributing scientific expertise to optimize target selection, modality choices, and pipeline

Qualifications

  • Hands-on preclinical R&D experience in-house developing drug assets.
  • Direct ADC/bispecific (or multispecific) antibody experience.
  • Work in a core R&D function: discovery biology, medicinal chemistry, protein/antibody engineering, computational, in vivo/translational, pharmacology/PK-PD or toxicology.
  • 5+ years of relevant industry R&D experience.

Responsibilities

  • Review technical materials and real-world R&D data across ADC and/or bispecific programs.
  • Label, classify, and annotate data and model outputs to improve AI model performance.
  • Provide expert perspective on target/modality selection, optimization, risk, and development decisions.
  • Help define structure and quality standards for the client's dataset pipeline.
  • Participate in expert discussions, interviews, and research engagements.
  • Work independently and asynchronously to meet deadlines while improving AI model performance.

Job description

Mercor connects elite creative and technical talent with leading AI research labs. Mercor currently seeks a Preclinical R&D Expert focusing on ADC and bispecifics for remote, hourly engagement.

The contractor will review complex R&D data, annotate model outputs, and assist in critical development decisions across discovery to preclinical stages. You will work independently in a fast-paced environment, contributing scientific expertise to optimize target selection, modality choices, and pipeline

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