Lead AI Scientist

Flagship Pioneering; Cambridge; Hybrid; GBP 80,000 - 110,000; Full time

Job summary

A biotechnology company is seeking a lead AI scientist to innovate computational capabilities for drug development in Cambridge, UK. The role demands strong leadership, proficiency in Python, and experience in AI/ML for target discovery. Ideal candidates will have a PhD and significant industry experience. This position offers hybrid work arrangements to suitable candidates.

Qualifications

• 3–7 years of industry experience delivering AI/ML systems for target discovery and/or drug development.
• Strong leadership and collaboration skills in a matrix environment.
• Development of methods leveraging sequencing technologies.

Responsibilities

• Collaborate to set the technical strategy and roadmap for AI across drug discovery.
• Lead and mentor the AI/ML team while maintaining quality standards.
• Develop methods leveraging next-generation sequencing for target discovery.

Skills

Leadership and collaboration

Proficiency in Python

Experience with AI/ML systems

Attention to detail

Cloud computing

Education

PhD in genomics, computational biology, computer science, or similar

Tools

AWS

Job description

Position Summary

We are seeking a lead AI scientist to invent and productionize computational capabilities that power target identification, validation, and drug development. The candidate is experienced and excited to operate in a deeply collaborative way with peers in data infrastructure, assay development, biology validation, and drug discovery. They bring enthusiasm, intellectual curiosity, scientific rigor, and a deep-rooted desire to innovate. The candidate will have Director level seniority and report to the head of assay development and data science. This role is based in the vicinity of Cambridge UK and hybrid work arrangements will be considered, but all applicants will require permission to work in UK.

Key Responsibilities

• Collaborate with key stakeholders to set the technical strategy and roadmap for AI across the platform and drug discovery, then define execution plans and deliver against them.
• Lead, mentor, and grow the AI/ML team, fostering a focus on collaboration and delivery, while maintaining standards for quality and reproducibility.
• Partner with Data Infrastructure to optimize data schemas, metadata, versioning, and access controls for ML training and inference.
• Develop and deploy methods leveraging next-generation sequencing for target discovery and validation.
• Develop and deploy methods leveraging imaging AI of histology images, such as segmentation/detection/classification, weak/self-supervised learning, and rigorous model evaluation tied to biological goals.
• Develop and deploy methods to accelerate the validation of targets using standardised assays and accelerate drug development.
• Communicate results and decisions clearly to technical and non-technical stakeholders.

Requirements

• PhD or equivalent experience in genomics, computational biology, computer science, or similar discipline is required.
• 3–7 years of industry experience delivering AI/ML systems for target discovery and/or drug development, with end‑to‑end ownership from method development to production.
• Strong leadership and collaboration skills in a matrix environment with a low‑ego, high‑ownership working style.
• High proficiency in Python and modern ML tooling, cloud computing environments (e.g., AWS), as well as solid software engineering habits (testing, CI/CD, containers, etc.).
• Priority domain experience:
  • Genomics/NGS: development of methods to leveraging sequencing technologies.
  • Imaging AI: microscopy/histology, WSI pipelines, dataset curation/labeling strategies, scalable training and evaluation.
  • Validation and drug discovery: development or application of AI tools to target validation using in‑vitro models and/or drug discovery in any modality.
• A high degree of energy, accuracy and attention to detail, and a passion for creating transformative medicines for patients with serious diseases.