Principal Scientist, Biologics AI

AstraZeneca•Waltham, MA

1d•$144,649 - $216,973•Hybrid

About The Position

We are seeking an experienced and visionary Associate Principal Scientist to lead Biologics AI innovation at AstraZeneca’s US R&D centers in Waltham, MA or Gaithersburg, MD. This is a high‑impact scientific leadership role accountable for defining and executing the AI strategy that integrates state‑of‑the‑art machine learning with wet‑lab discovery to accelerate biologics engineering and enable next‑generation biotherapeutics. You will set technical direction, own delivery across multiple programs, and shape data generation at scale—working across computational and experimental functions and with global partners to translate AI into robust, reproducible advances in discovery.

Requirements

PhD in computer science, machine learning, bioinformatics, computational biology, physics, chemistry, mathematics, engineering, or a related quantitative field, with typically 8+ years of relevant post‑degree experience in academia and/or industry; or a Master’s with 12+ years of relevant experience.
Demonstrated track record applying AI/ML to proteins, antibodies, or related biologics, with clear examples of methods translated into experimental outcomes, platform capabilities, or pipeline decisions.
Hands‑on leadership in developing and deploying advanced ML (deep learning, generative models, structure‑aware and geometric methods, sequence/structure multi‑modal models) for protein sequence modeling, structure‑informed prediction, de novo design, or biologics optimization.
Proven success establishing iterative computational–experimental cycles (e.g., active learning, design–build–test–learn), including designing experiments to interrogate model predictions and improve data/model quality.
Experience leading the full ML lifecycle at scale—data design and preprocessing, model architecture, training/evaluation, deployment, monitoring, and maintenance—using modern ML frameworks (e.g., PyTorch, TensorFlow) and software engineering best practices.
Experience with cloud‑based ML environments and scalable data workflows; ability to specify requirements and partner with data engineering/IT to evolve production ML systems that support discovery at scale.
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 with the ability to synthesize complex technical concepts for diverse audiences and to 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 with antibody/nanobody/protein engineering, including de novo design and multi‑objective optimization for developability, stability, and functional performance.
Expertise with generative models (e.g., diffusion, autoregressive LMs), geometric deep learning/graph neural networks, Bayesian optimization, uncertainty quantification, and active learning for guided experimentation.
Experience integrating heterogeneous data types (sequence, structure, biophysics/biochemistry assays, high‑throughput binding/functional data, bioprocess/developability metrics) into unified models.
Experience leading deployment of scientific software/ML models into production discovery workflows, including model monitoring, versioning, and compliance with governance standards.
Demonstrated ability to design or refine assay strategies and experimental campaigns to maximize downstream ML performance and data reuse, including metadata standards and FAIR principles.
Prior experience leading scientists and managing complex projects or collaborations; ability to set goals, delegate effectively, and deliver against timelines.
Strong external scientific presence (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 deliver measurable impact on pipeline goals.
Lead multiple cross‑functional discovery initiatives from problem framing through deployment, ensuring rapid translation of computational insights into experimental design and decision‑making.
Architect, develop, and guide application of cutting‑edge models—protein language models, structure‑informed and geometric methods, de novo/protein design, and multi‑modal learning that fuses sequence, structure, and biological activity data—to solve high‑value scientific problems.
Establish closed‑loop design–build–test–learn workflows with experimental teams, formalizing feedback cycles, uncertainty quantification, and active learning to improve model reliability and throughput.
Set standards for high‑quality data generation, curation, and metadata; partner with wet‑lab leaders to design assays and campaigns that maximize ML utility and reproducibility; influence data platform evolution in collaboration with informatics and engineering.
Oversee and improve processes across data pipelines, model development, validation, deployment, monitoring, and continuous improvement, including best practices for reproducibility, documentation, and scientific rigor.
Mentor and upskill scientists across AI/ML and experimental domains; provide day‑to‑day technical guidance and contribute to recruitment and development of a high‑performing team.
Communicate strategy, progress, risk, 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; evaluate and integrate emerging methods and tools.