Director, Scientific Cloud Engineering

Flagship Pioneering, Inc.•Cambridge, MA

18h•$172,000 - $236,500

About The Position

Flagship Pioneering is seeking a Director, Scientific Cloud Engineering to lead the cloud and computational infrastructure for scientific discovery across its portfolio. This senior technical leader will manage a team of engineers, including Cloud DevOps and Bioinformatics, and serve as the primary technical authority for scientific and scientific computing platforms. The role requires a blend of cloud architecture expertise and a deep 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, with 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, not just bespoke solutions.
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 the Scientific Cloud Engineering team, including external contractors and consultants; set a high technical bar and foster 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 as the portfolio scales.
Establish engineering best practices, code review standards, and documentation norms across their Scientific Cloud Engineering team.
Own and evolve the cloud architecture strategy for Scientific Cloud, designing scalable, secure, and cost-efficient infrastructure across AWS and/or Azure environments.
Architect and deliver reusable infrastructure-as-code (IaC) solutions — including Terraform modules, CDK constructs, and containerized service templates — that portfolio companies can adopt without custom engineering.
Partner with Infrastructure, Operations, and Architecture (IO&A) to define and enforce cloud governance guardrails: identity and access management (IAM), networking, secrets management, cost tagging, and security posture.
Design and maintain HPC and compute environments — including Nextflow/Cromwell execution layers, Kubernetes clusters, and GPU-enabled instances — optimized for scientific workloads.
Evaluate and adopt emerging cloud capabilities, including serverless orchestration, data lakehouse patterns, and managed ML platforms (e.g., AWS SageMaker, Azure ML).
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 (Nextflow, Snakemake), container registries, reference data management, and secure data ingestion from external sources (e.g., UK Biobank, dbGaP, controlled-access repositories).
Ensure compute environments support the full spectrum of scientific compute: interactive analysis (Jupyter, RStudio), 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 — ensuring consistency across all Flagship portfolio companies and research teams.
Design and publish reusable infrastructure products, reference architectures, and deployment templates that enable portfolio companies to launch and scale scientific capabilities quickly.
Serve as the technical standards interface for internal teams and portfolio companies, reviewing proposed architectures and ensuring alignment with Flagship IT guardrails.
Collaborate with the Pioneering Intelligence (PI) team and portfolio companies to enable and accelerate AI/ML capabilities — including model training infrastructure, feature stores, vector databases, and LLM deployment patterns.
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 the integration and performance requirements of ELN platforms, LIMS, scientific instruments, and research applications.
Engage with IT Security and Compliance to maintain a strong cloud security posture, including SOC 2, GxP, and HIPAA-relevant requirements as the portfolio matures.
Work with portfolio company CTOs and heads of data science and bioinformatics to understand scientific infrastructure needs and ensure Scientific Cloud is delivering ahead of demand.