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10 дней назад

Research Leader (DMPK)

Формат работы
hybrid
Тип работы
fulltime
Грейд
lead
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Research Leader (DMPK) (AI drug discovery): Designing and implementing AI-first DMPK strategies, screening cascades, and quantitative pharmacology packages for small-molecule drug discovery with an accent on ADME, biotransformation, PK modelling, and toxicology. Focus on integrating DMPK, pharmacodynamic, and toxicological data, predicting human pharmacokinetics and safety windows, and guiding molecule design toward clinical candidates.

Location: Cambridge, Massachusetts, United States; hybrid work with attendance in the office 3 days per week

Company

hirify.global applies frontier AI and machine learning to drug discovery, developing predictive and generative models for molecular design and therapeutic research.

What you will do

  • Design and implement stage-appropriate, AI-first DMPK strategies integrated with quantitative pharmacology and project objectives.
  • Design screening cascades and mechanistic experiments to support data-driven drug discovery decisions.
  • Integrate DMPK, pharmacodynamic, and toxicological data to predict doses and safety windows.
  • Define quantitative pharmacology strategies with Pharmacology Leads and collaborate with Preclinical, Clinical, Chemistry, and Technology teams.
  • Optimise datasets and molecule design using physico-chemical and biotransformation knowledge.
  • Author DMPK sections of regulatory documents and advance methods for human PK prediction.

Requirements

  • PhD in Pharmaceutical Science, Pharmacy, Chemistry, or a related field.
  • 7–10+ years of professional DMPK experience in a biotech or pharmaceutical setting.
  • Experience developing DMPK and quantitative pharmacology packages for early clinical development.
  • Experience authoring and reviewing regulatory documents and partnering with Preclinical and Clinical teams.
  • In-depth knowledge of biotransformation, PK modelling, and scaling of ADME properties.
  • Ability to work from the Cambridge, MA office 3 days per week.

Nice to have

  • Experience with peptides and other new modalities.
  • Advanced knowledge of PK/PD modelling.
  • Understanding of machine learning techniques.
  • Familiarity with Python and R.

Culture & Benefits

  • Hybrid collaboration model focused on knowledge sharing and in-person relationships.
  • Culture guided by thoughtful, brave, determined, and collaborative work.
  • Emphasis on inclusion, continuous professional development, shared learning, and employee support.
  • Equal employment opportunity and workplace accommodation support.

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