Biologics AI Director — Lead AI-Driven Discovery

AstraZeneca plc; Cambridge (MA); Presencial; USD 186.000 - 279.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Retirement programs

Paid time off

Health, dental, and vision coverage

Descripción de la vacante

AstraZeneca plc in Cambridge, MA invites an experienced Director, Biologics AI to lead AI innovation across biologics discovery. You will define the AI strategy, own multiple programs, and shape data generation at scale, aligning computational and experimental work to accelerate bioscience breakthroughs.

You will architect state-of-the-art ML models (protein language, structure-aware, multi-modal) and establish closed-loop design–build–test–learn workflows with wet-lab teams, delivering

Formación

• PhD with 8+ years post-degree in relevant field; or a Master’s with 12+ years.
• Proven AI/ML work on proteins/biologics with experimental impact.
• Experience leading end-to-end ML lifecycles at scale.
• Strong cross-functional leadership and communication.

Responsabilidades

• Define and drive AI strategy for biologics discovery.
• Lead cross-functional discovery programs from framing to deployment.
• Architect and guide cutting-edge ML models for protein design.
• Establish closed-loop AI–wet-lab workflows.
• Mentor scientists and oversee team development.

Conocimientos

AI strategy

Cross-functional leadership

Protein ML models

MLOps / deployment

Cloud ML environments

Scientific communication

Mentorship

Active learning

Educación

PhD in CS/ML/bioinformatics

Master's +12 years experience

Herramientas

PyTorch

TensorFlow

Geometric deep learning

Descripción del empleo

AstraZeneca plc in Cambridge, MA invites an experienced Director, Biologics AI to lead AI innovation across biologics discovery. You will define the AI strategy, own multiple programs, and shape data generation at scale, aligning computational and experimental work to accelerate bioscience breakthroughs.

You will architect state-of-the-art ML models (protein language, structure-aware, multi-modal) and establish closed-loop design–build–test–learn workflows with wet-lab teams, delivering