Director, Scientific Cloud Engineering

Flagship Pioneering•Cambridge, MA

About The Position

Flagship Pioneering is seeking a Director, Scientific Cloud Engineering to lead the engineering of cloud and computational infrastructure for scientific discovery across its portfolio of companies. This senior technical leader will manage a team of engineers, including Cloud DevOps and Bioinformatics, and be the primary technical authority for scientific computing platforms. The role requires a blend of cloud architecture expertise and an understanding of biotech research computational demands, including genomics workflows, HPC, containerized pipelines, and AI/ML infrastructure. The Director will collaborate with Data Architecture & Engineering, Lab Systems, and Research Systems teams to ensure scalable, reproducible, and scientifically grounded infrastructure.

Requirements

10+ years of experience in cloud engineering, infrastructure engineering, or DevOps.
At least 3 years in a technical leadership or engineering management role.
Deep expertise in AWS (preferred) and/or Azure, including compute, storage, networking, IAM, and managed services for data and ML.
Demonstrated experience designing and operating infrastructure for scientific or biotech workloads (genomics pipelines, HPC, -omics data processing, or equivalent).
Strong proficiency in infrastructure-as-code (Terraform, CDK, CloudFormation) and CI/CD automation (GitHub Actions, Jenkins, or similar).
Experience with containerization and orchestration technologies (Docker, Kubernetes, ECS/EKS).
Familiarity with bioinformatics workflow frameworks (Nextflow, Snakemake, WDL) and scientific compute environments.
Background in biotech, life sciences, or pharmaceutical research through education (e.g., B.S./M.S./Ph.D. in computational biology, bioinformatics, biochemistry, or related field) or direct industry experience in a scientific computing context.
Track record of building and shipping reusable infrastructure products and reference architectures.
Strong communicator who can translate between scientific requirements and engineering specifications, and present technical strategy to non-technical stakeholders.

Nice To Haves

Experience operating in a multi-tenant or portfolio model supporting multiple independent teams or companies on shared infrastructure.
Familiarity with AI/ML infrastructure (model training pipelines, MLflow or similar experiment tracking, SageMaker, or LLM deployment on cloud).
Experience with data lakehouse architectures (Delta Lake, Iceberg, or equivalent) and their integration with scientific data types.
Exposure to GxP, HIPAA, or SOC 2 compliance requirements in a cloud context.
Prior experience at a biotech incubator, multi-portfolio organization, or venture-backed life sciences company.

Responsibilities

Lead, mentor, and grow a team of engineers, including external contractors and consultants, fostering a culture of rigor, collaboration, and continuous improvement.
Serve as a player-coach, providing direct technical contributions while managing team capacity, priorities, and professional development.
Partner with the Senior Director of Scientific Cloud to define team roadmap, resource allocation, and hiring strategy.
Establish engineering best practices, code review standards, and documentation norms.
Own and evolve the cloud architecture strategy for Scientific Cloud, designing scalable, secure, and cost-efficient infrastructure across AWS and/or Azure.
Architect and deliver reusable infrastructure-as-code (IaC) solutions, such as Terraform modules and CDK constructs.
Partner with Infrastructure, Operations, and Architecture (IO&A) to define and enforce cloud governance guardrails (IAM, networking, secrets management, cost tagging, security posture).
Design and maintain HPC and compute environments, including Nextflow/Cromwell execution layers, Kubernetes clusters, and GPU-enabled instances.
Evaluate and adopt emerging cloud capabilities, including serverless orchestration, data lakehouse patterns, and managed ML platforms.
Partner with data scientists and bioinformaticians to architect robust, reproducible pipeline infrastructure for genomics, proteomics, and other -omics data types.
Oversee the design and deployment of bioinformatics platform capabilities, including workflow orchestration, container registries, reference data management, and secure data ingestion.
Ensure compute environments support interactive analysis, large-scale batch processing, and real-time ML inference.
Champion FAIR data principles and reproducibility standards in scientific compute infrastructure.
Develop and steward portfolio-wide engineering standards for cloud infrastructure, security, and scientific compute.
Design and publish reusable infrastructure products, reference architectures, and deployment templates.
Serve as the technical standards interface for internal teams and portfolio companies.
Collaborate with the Pioneering Intelligence (PI) team and portfolio companies to enable and accelerate AI/ML capabilities.
Collaborate closely with the Data Architecture & Engineering team to align on data platform design, pipeline standards, and lakehouse infrastructure.
Partner with Lab Systems and Research Systems teams to ensure cloud infrastructure meets integration and performance requirements.
Engage with IT Security and Compliance to maintain a strong cloud security posture.
Work with portfolio company CTOs and heads of data science and bioinformatics to understand scientific infrastructure needs.