Flagship Pioneering, Inc. - Director, Scientific Cloud Engineering

• Team Leadership & Engineering Management • Lead, mentor, and grow , 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. • Cloud Architecture & Infrastructure • 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). • Scientific Computing & Bioinformatics Platforms • and data scientists 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. • Standards, Products & Portfolio Enablement • 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. • Cross-Functional Collaboration • 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.