Head of Engineering, AI for Drug Discovery

Roche

Head of Engineering leading AI initiatives within Roche’s AI for Drug Discovery group. Overseeing ML Engineering and infrastructure to accelerate pharmaceutical research and development.

Posted 7/15/2026full-timeSouth San Francisco • California, New York • 🇺🇸 United StatesLead💰 $229,900 - $426,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in leading multi-disciplinary engineering teams, with a focus on developing scalable and reliable machine learning platforms and data products. Proficient in cloud computing environments and the lifecycle of AI model development.

Highest-signal resume keywords

Machine Learning EngineeringData Engineering LeadershipCloud Computing (AWS, GCP, Azure)Infrastructure for AI WorkflowsTechnical Strategy Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringData Product DesignMachine Learning AlgorithmsModel Evaluation TechniquesData Pipeline Architecture

Soft Skills

People LeadershipCollaborationStrategic Thinking

Industry Keywords

AIML PlatformsComputational SystemsResearcher-Facing Applications

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud Platform

About the role

Key responsibilities & impact

Own technical strategy and roadmap for the AI4DD engineering function.
Lead the ML Engineering and Infrastructure team designing and maintaining robust ML platforms.
Partner closely with LLM and Agent research teams.
Oversee the Data Engineering team's efforts in the architecture and development of data pipelines.
Direct the Product Engineering team’s development of researcher-facing applications.

Requirements

What you’ll need

At least 10 years of experience in software, data or ML engineering, with at least 5 years in a people leadership role managing multi-disciplinary engineering teams.
Experience in designing scalable, reliable, and cost-effective data products and computational systems.
Extensive hands-on experience building, scaling, and maintaining infrastructure for machine learning/AI workflows.
Deep expertise in cloud computing environments (e.g., AWS, GCP, Azure).
Deep understanding of machine learning algorithms, model evaluation techniques, and the infrastructure and lifecycle of developing AI models.

Benefits

Comp & perks

Relocation benefits are available for this opportunity