Hybrid Director, AI – ML Infrastructure

at Allen Institute

• Job title: Director, AI – ML Infrastructure
• Job type: Full Time
• Experience level: Lead
• Salary: $209,550 - $274,950 per year
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Azure, Cloud, Google Cloud Platform
• Location requirements: Hybrid, Seattle, United States

About the role

• AI/ML Infrastructure Architect at the Allen Institute developing engineering infrastructure for AI/ML applications. Collaborating with cross-functional teams to support bioscience research.

Responsibilities

• Develop and lead a cloud-agnostic state-of-the-art engineering infrastructure at the Allen Institute to support AI/ML research and applications.
• Procure and deploy GPUs to meet computational demands.
• Coordinate infrastructure implementation with external partners.
• Lead data management, software infrastructure and AI/ML workflow best practices and policies.
• Manage and lead a team of engineers.
• Develop and implement policies and software for efficient management, prioritization, and scheduling of AI workloads.
• Implement Cost Tracking and Reporting for transparency and prevent overruns.
• Collaborate with science unit teams to facilitate adoption and use of the new AI pipeline by providing training and support to accelerate the adoption process.
• Ensure integration of AI infrastructure with existing platforms.
• Develop and oversee a governance framework to ensure use of GPU resources align with the institutes scientific priorities.
• Regularly review and adjust resource allocation based on governance inputs.
• Help establish community standards for scalability in developing, disseminating, and evaluating AI/ML/computational methods for scientific problems.
• Participate in institute-wide initiatives, workshops, and seminars to promote engineering excellence through technical leadership, cross-disciplinary collaboration and knowledge sharing.

Requirements

• Bachelors Degree in Computer Engineering or related technical field or equivalent experience
• 7 years of experience working with MLOps in medium to large scale GPU clusters and/or cloud based ML deployments
• Experience with building, deploying and maintaining machine learning models
• Proficiency with cloud computing (AWS, GCP or Azure) and with on-prem clusters
• Experience with databases, large data management
• Working knowledge of AI/ML custom libraries, AI/ML execution platforms
• Proven ability to work independently and manage multiple projects simultaneously while meeting deadlines
• Excellent written and verbal communication skills, with the ability to collaborate effectively in a multidisciplinary team environment.

Benefits

• medical, dental, vision, and basic life insurance
• 401k plan
• Paid time off