Director, Biologics AI

AstraZeneca•Cambridge, MA

2d•$186,014 - $279,022•Onsite

About The Position

AstraZeneca is seeking a Director, Biologics AI to lead biologics AI innovation at its US R&D center in Cambridge, Massachusetts. This scientific leadership role is accountable for defining and executing an AI strategy that combines state-of-the-art machine learning with wet-lab discovery to accelerate biologics engineering and enable next-generation biotherapeutics. The role sets technical direction, owns delivery across multiple programs, shapes large-scale data generation, and works across computational and experimental functions and with global partners to translate AI into robust, reproducible advances in discovery. The Director leads cross-functional discovery initiatives from problem framing through deployment; advances methods including protein language models, structure-informed and geometric approaches, de novo protein design, and multimodal learning; and establishes closed-loop design–build–test–learn workflows. The position also sets data generation and governance standards, oversees the end-to-end ML lifecycle, mentors and develops scientists, influences stakeholders and portfolio decisions, and represents AstraZeneca through scientific collaborations, publications, patents, and external venues. The position is based at AstraZeneca’s Cambridge, MA R&D center. The company typically works from the office to support in-person collaboration while supporting flexibility where possible. The annual base pay range is $186,014 to $279,022. The role is eligible for short-term incentive bonuses and equity-based awards. Benefits include qualified retirement programs, paid time off (vacation, holidays, and leaves), and health, dental, and vision coverage, subject to applicable plan terms.

Requirements

PhD in computer science, machine learning, bioinformatics, computational biology, physics, chemistry, mathematics, engineering, or a related quantitative field, with typically 8 or more years of relevant post-degree experience in academia and/or industry; alternatively, a Master’s degree with 12 or more years of relevant experience.
Demonstrated track record applying AI/ML to proteins, antibodies, or related biologics, with examples of methods translated into experimental outcomes, platform capabilities, or pipeline decisions.
Hands-on leadership in developing and deploying advanced ML, including deep learning, generative models, structure-aware and geometric methods, and sequence/structure multimodal models for protein modeling, prediction, design, or optimization.
Proven success establishing iterative computational–experimental cycles, such as active learning or design–build–test–learn, including designing experiments to interrogate model predictions and improve data and model quality.
Experience leading the full ML lifecycle at scale, including data design and preprocessing, model architecture, training and evaluation, deployment, monitoring, and maintenance.
Experience using modern ML frameworks such as PyTorch or TensorFlow and software engineering best practices.
Experience with cloud-based ML environments and scalable data workflows, and ability to specify requirements and partner with data engineering/IT on production ML systems.
Strong record of influencing and delivering in matrixed, multidisciplinary environments, bridging AI scientists, computational biologists, protein engineers, and wet-lab teams across sites.
Excellent communication skills, including the ability to explain complex technical concepts to diverse audiences and shape scientific and portfolio decisions.
Evidence of scientific innovation and impact through publications, patents, platform creation, or deployment of AI methods that materially improved experimental or business outcomes.

Nice To Haves

Experience in antibody, nanobody, or protein engineering, including de novo design and multi-objective optimization for developability, stability, and functional performance.
Expertise in generative models such as diffusion models or autoregressive language models; geometric deep learning or graph neural networks; Bayesian optimization; uncertainty quantification; and active learning for guided experimentation.
Experience integrating heterogeneous data, including sequence, structure, biophysics or biochemistry assays, high-throughput binding or functional data, and bioprocess or developability metrics, into unified models.
Experience leading production deployment of scientific software or ML models into discovery workflows, including monitoring, versioning, and governance compliance.
Demonstrated ability to design or refine assay strategies and experimental campaigns to improve downstream ML performance and data reuse, including metadata standards and FAIR principles.
Prior experience leading scientists and managing complex projects or collaborations, setting goals, delegating, and delivering to timelines.
Strong external scientific presence through peer-reviewed publications, patents, invited talks, open-source contributions, or community standards.

Responsibilities

Define and drive the AI strategy for biologics discovery and engineering, setting priorities and roadmaps that integrate AI and wet-lab capabilities and support pipeline goals.
Lead multiple cross-functional discovery initiatives from problem framing through deployment, translating computational insights into experimental design and decision-making.
Architect, develop, and guide advanced ML methods, including protein language models, structure-informed and geometric methods, de novo protein design, and multimodal learning across sequence, structure, and biological activity data.
Establish closed-loop design–build–test–learn workflows with experimental teams, including feedback cycles, uncertainty quantification, and active learning.
Set standards for data generation, curation, and metadata; partner with wet-lab leaders on assays and campaigns; and influence data platform evolution with informatics and engineering teams.
Oversee and improve the end-to-end ML lifecycle, including data pipelines, model development, validation, deployment, monitoring, and continuous improvement, with emphasis on reproducibility, documentation, and scientific rigor.
Mentor and upskill scientists across AI/ML and experimental domains, provide technical guidance, and contribute to recruitment and team development.
Communicate strategy, progress, risks, and scientific insights to senior stakeholders; influence portfolio decisions and advocate for AI-enabled approaches internally and with external partners.
Drive publications, patents, and external visibility; represent AstraZeneca in collaborations and at scientific venues; and evaluate and integrate emerging methods and tools.