Flagship Pioneering
Director, Scientific Cloud Engineering
$172K–$236K
Cambridge, MA USA +1 · Director · Full-time · Posted Sep 25, 2026
- Location
- Cambridge, MA USA
Cambridge, Massachusetts, United States - Experience
- 10+ years in cloud engineering, infrastructure engineering, or DevOps; at least 3 years in technical leadership or engineering management.
- Education
- B.S./M.S./Ph.D. in computational biology, bioinformatics, biochemistry, or a related field, or direct industry experience in a scientific computing context.
About the job
Leads the team and designs reusable cloud and scientific computing infrastructure for research across Flagship’s portfolio companies.
What you'll do
- Lead and mentor engineers, contribute technically, and set engineering practices and team priorities.
- Own cloud architecture and deliver secure, scalable infrastructure, including infrastructure-as-code, HPC, and containerized environments.
- Set portfolio-wide infrastructure standards and work with scientific, data, and portfolio teams to support reproducible research and AI/ML capabilities.
What they ask for
- 10+ years in cloud engineering, infrastructure engineering, or DevOps, including at least 3 years in technical leadership or engineering management.
- Deep expertise in AWS and/or Azure; experience designing and operating infrastructure for scientific or biotech workloads.
- Strong proficiency in infrastructure-as-code and CI/CD automation; experience with containerization and orchestration.
- Background in biotech, life sciences, or pharmaceutical research through education or direct industry experience in scientific computing.
Nice to have
- Experience supporting multiple teams or companies on shared infrastructure.
- Familiarity with AI/ML infrastructure, data lakehouse architectures, or cloud compliance requirements.
Pay
$172,000 - $236,500 USD base salary
Tools
AWS, Azure, Terraform, CDK, CloudFormation, GitHub Actions, Jenkins, Kubernetes
AI focus
Enable AI/ML capabilities, including model training infrastructure, feature stores, vector databases, LLM deployment patterns, and real-time ML inference.
Model-written summary · the original posting is authoritative