Therapeutics Architect

THE REMIT OF THE BIOLOGIST IS CHANGING 

Drug discovery and development is a decision-making problem. We are generating more data and building better models, but real impact comes from knowing which questions matter and what evidence actually impacts a program’s trajectory. We need scientists who can pair model-driven outputs with sharp therapeutic judgment to figure out which therapies should exist, and for whom. 

In this role, you’ll turn those outputs into evidence-based recommendations for therapeutic programs. This means being deep in biology and therapeutic concepts, while also being genuinely engaged with frontier AI and how that is changing how we work. You’ll work alongside experienced drug discovery and development colleagues to build and test better ways of working.

THE OPPORTUNITY 

You'll join a cross-functional therapeutics team responsible for surfacing promising therapeutic opportunities, pressure-testing their scientific and translational logic, and shaping how programs move through the pipeline. We're intentionally building a different kind of therapeutics group to support this work, because turning human foundation models / insights into new therapeutic concepts and real patient impact requires a fundamentally different way of working.

The people who will thrive here bring scientific depth, curiosity, and an AI-native perspective, incorporating agentic tools and machine intelligence into how they reason through problems. They ask what an AI-native platform can reveal and what those insights mean for therapeutic decisions. They want to develop their ability to distinguish an interesting hypothesis from a viable therapeutic concept and to weigh opportunities beyond their own project. This is a hands-on individual contributor role suited to scientists building their careers in drug discovery; you don’t need to have led a portfolio or taken a program from discovery to the clinic.

This is a hybrid role based in New York, with ~three days per week in the office.

Key Responsibilities:

• Evaluate biological insights and therapeutic opportunities, taking ownership of focused analyses and developing recommendations with the therapeutics team.
• Work cross-functionally to move programs forward by identifying key scientific questions, proposing decision criteria, and contributing to the next steps
• Develop platform-informed hypotheses around therapeutic targets, mechanisms, patient populations, biomarkers, and clinical strategy
• Build evidence packages that inform investment, partnering, and development decisions
• Design and improve AI-enabled scientific workflows that increase the durability and quality of therapeutic decision-making
• Compare therapeutic opportunities and contribute to team prioritization by assessing biological rationale, technical feasibility, clinical impact, and strategic fit

WHAT WE’RE LOOKING FOR

The strongest candidates are curious across disciplines and comfortable working through uncertainty. They use AI tools to explore ideas, challenge assumptions, and surface hypotheses, then bring scientific judgment to the results: checking evidence, recognizing weak or circular reasoning, and distinguishing plausible outputs from meaningful ones. They bring depth in a relevant area of biology and an interest in broadening their therapeutic judgment. They communicate their reasoning clearly, seek feedback, and adapt when the evidence changes.

Qualifications:

• PhD, MD, or equivalent experience in cancer biology, immunology, pharmacology, molecular biology, translational medicine, or a related discipline
• Hands-on research experience relevant to drug discovery or translational science, gained through doctoral or postdoctoral research, industry collaborations, or an initial industry role. You can explain how your work informed a therapeutic hypothesis, target-validation decision, or next experiment; prior program leadership is not required
• A strong foundation in disease biology and the ability to connect mechanistic evidence to therapeutic hypotheses, with an interest in building knowledge of modality selection, translational strategy, and clinical positioning
• Curiosity and a collaborative approach to working with colleagues across biology, machine learning, computational science, and strategy
• Hands-on use of modern AI tools in scientific work, with examples of how they’ve changed the way you investigate questions, evaluate evidence, or make therapeutic decisions 
• A systems-thinking mindset and the ability to explain trade-offs between scientific hypotheses or experiments
• Sound scientific judgment, clear communication, and the ability to explain evidence, uncertainty, and recommendations to colleagues across disciplines
• Bonus: experience with computational biology, data science, machine learning, or programming (Python, R, SQL), though deep biological reasoning and therapeutic judgment are more important than any particular technical stack