CZI is looking to solve the business and technical problem of accelerating scientific discovery in life sciences by building and managing one of the largest computing systems dedicated to AI research in the world. This involves developing predictive models of healthy and diseased cells to help cure, prevent, or manage diseases by the end of the century.
Requirements
- Strong knowledge of AI/ML frameworks (e.g., PyTorch) and MLOps tools (e.g., Kubeflow, MLflow, Ray).
- Experience managing both traditional cloud platforms (AWS, GCP, Azure) and AI cloud (HPC/GPU clusters).
- Deep experience with large-scale data systems, pipelines, and storage technologies.
- Track record of improving reliability, observability, and cost efficiency in large-scale systems.
- Proven ability to define multi-year infrastructure strategies while delivering on immediate priorities.
- Ability to provide clear leadership and momentum in an ambiguous environment—setting direction, aligning teams, and turning uncertainty into forward progress.
Responsibilities
- Define and execute the long-term vision and roadmap for AI, data, cloud, and security infrastructure, with clear metrics to measure progress and outcomes.
- Oversee the design and operation of hybrid GPU compute clusters and ML platforms to support training, fine-tuning, and inference workloads.
- Ensure robust, scalable, and efficient data infrastructure and cloud operations to power analytics, ML pipelines, and product needs.
- Drive reliability, observability, and cost optimization across GPU based workloads for development, training and inference.
- Implement modern AI/ML Ops practices (orchestration of model training workloads, reproducibility, automated monitoring) to accelerate research and production workflows, with a focus on continuous delivery and improvement.
- Build, mentor, and scale high-performing, multi-disciplinary engineering teams.
- Partner with product, research, and executive leadership to align infrastructure with organizational priorities, ensuring delivery is measured against agreed objectives and key results.
Other
- 15+ years in engineering, with at least 7+ years in senior leadership roles managing multi-disciplinary teams and organizations of 30+ employees, with experience leading and developing managers
- Exceptional written and verbal communication skills, capable of engaging technical and non-technical audiences.
- This role is a hybrid position requiring you to be onsite for at least 60% of the working month, approximately 3 days a week, with specific in-office days determined by the team’s manager.
- Ability to provide clear leadership and momentum in an ambiguous environment—setting direction, aligning teams, and turning uncertainty into forward progress.
