Chief Legal Officer

Hybrid- 3 days a week on site in New York Clinical-stage oncology programmes, a co-development deal with a top-five pharma, and a legal function that is essentially a blank page. If you have spent a decade building legal infrastructure in biotech and want to do it once more, at the frontier of how AI is reshaping drug development, this is that role. The Company Most biotechs describe themselves as data-driven. This company was built the other way round: the AI and technology platform came first, and the drug development operation was constructed around it. The platform reads across DNA, RNA, pathology, and clinical text of multimodal data, and it informs every major portfolio decision: which asset to advance, which trial design to change, which programme to stop. The operating model is deliberately different. There is no middle management and no approval hierarchy. Every function, legal included, runs as a small team of deep experts who command AI agents to do the volume work, leaving the humans to make the high-judgment calls. A team of 40 to 50 AI operators does the work of a traditional biotech of 300. That is not an aspiration; it is how the company runs today. The Opportunity You would be the first life-sciences-trained Chief Legal Officer this company has had, which means you are building the function rather than inheriting it. You will sit on the executive team and report directly to the CEO. The three loads you carry are: keeping the platform compliant with FDA AI/ML guidance, EMA, MHRA, and any emerging AI-disclosure requirements; structuring and closing the deals that will define the company's next chapter; and running a lean, AI-native legal function that delivers counsel-grade output. What You'll Be Doing Build and lead a small team of legal operators, each working with a suite of AI agents to handle contract review, IP monitoring, regulatory tracking, and diligence at a pace that outperforms teams many times its size. Design and own the compliance framework for an AI-driven clinical decision platform, mapping it against FDA AI/ML guidance, EMA, MHRA, and evolving AI-labour disclosure standards. Own the decision audit trail that ties every AI-informed clinical or portfolio decision to a human reviewer, a signed rationale, and a versioned model reference, so any regulator can inspect it on demand. Lead legal preparation for regulatory inspection of the platform, anticipating FDA scrutiny of AI-driven clinical systems before it arrives. Structure and close in-licensing, out-licensing, co-development, M&A, and strategic partnership transactions, coordinating across multiple counterparties who often appear in more than one workstream simultaneously. Manage external counsel as a targeted resource for the moves that cannot be made in-house, deep biotech-licensing markup, cross-border corporate work, litigation, holding them accountable for output quality and speed. Own board, shareholder, and subsidiary governance across the company's wholly-owned and majority-owned subsidiaries, with more planned. Develop IP strategy for the AI platform and the oncology pipeline, deciding where trade-secret protection is appropriate and where patent or publication better advances the company's position. Act as a strategic peer to the CEO, providing direct, reasoned challenge on legal risk and representing the company in health-authority, investor, and partner-CEO conversations where legal judgement is the deciding input. What We’re Looking For JD with active bar admission, plus 10 or more years of in-house legal leadership in biotech, pharma, and tech at General Counsel, Deputy GC, or Head of Legal level, with direct experience advancing clinical assets. Deep working knowledge of the clinical development lifecycle, including GCP, GLP, GMP, and FDA regulatory requirements across IND, NDA, and BLA processes. Demonstrated track record structuring and closing complex biotech transactions: co-development agreements, licensing, M&A, subsidiary acquisitions, and cross-border deals. Genuine fluency with AI and ML platforms in a drug-development context, sufficient to review contracts where AI outputs are the deliverable, understand model cards, and analyse data processing agreements that involve federated learning. #J-18808-Ljbffr elemed