Director, Scientific Cloud Engineering

Description

The company invents and builds life sciences ventures. It has originated more than 100 companies since 2000, including Moderna, and has put over $4 billion toward scientific discovery.

The Scientific Cloud team is the IT group behind the portfolio. It covers cloud engineering, data and informatics engineering, research systems, lab systems, and vendor management, and builds shared infrastructure that each company can rely on from formation through growth.

This director role leads cloud and compute engineering across the portfolio and reports to the Senior Director of Scientific Cloud. You will manage a growing group of engineers, including Cloud DevOps and Bioinformatics, and act as the main technical authority for scientific computing platforms. You will also keep contributing hands-on technical work.

The role calls for deep cloud architecture skill and a working grasp of biotech compute needs such as genomics, HPC, containerized pipelines, and AI/ML infrastructure. You will work with Data Architecture & Engineering, Lab Systems, and Research Systems so that what gets built scales, reproduces results, and fits real scientific work.

Responsibilities

• Lead, mentor, and grow engineers, including external contractors and consultants. Set a high technical bar and build a culture of rigor, teamwork, and ongoing improvement.
• Write code and design solutions yourself while also handling team capacity, priorities, and professional development.
• With the Senior Director of Scientific Cloud, set the team roadmap, resource allocation, and hiring plan as the portfolio grows.
• Create engineering best practices, code review standards, and documentation norms for the Scientific Cloud Engineering team.
• Own and evolve the cloud architecture strategy, designing infrastructure that is scalable, secure, and cost-efficient on AWS, Azure, or both.
• Build reusable infrastructure-as-code solutions, such as Terraform modules, CDK constructs, and containerized service templates, that portfolio companies can adopt without custom work.
• With Infrastructure, Operations, and Architecture (IO&A), set and enforce cloud governance guardrails covering IAM, networking, secrets management, cost tagging, and security posture.
• Design and run HPC and compute environments, including Nextflow or Cromwell execution layers, Kubernetes clusters, and GPU instances, tuned for scientific workloads.
• Assess and adopt new cloud capabilities such as serverless orchestration, data lakehouse patterns, and managed ML platforms like AWS SageMaker or Azure ML.
• Work with scientists and data scientists to build reproducible pipeline infrastructure for genomics, proteomics, and other -omics data.
• Oversee bioinformatics platform capabilities: workflow orchestration with Nextflow or Snakemake, container registries, reference data management, and secure ingestion of external data from sources such as UK Biobank, dbGaP, and other controlled-access repositories.
• Make sure compute environments support interactive analysis in Jupyter or RStudio, large batch jobs, and real-time ML inference.
• Promote FAIR data principles and reproducibility in scientific compute infrastructure.
• Write and publish portfolio-wide standards for cloud infrastructure, security, and scientific compute so every company and research team stays consistent.
• Create reusable infrastructure products, reference architectures, and deployment templates that help companies launch and scale scientific capabilities quickly.
• Act as the technical standards contact for internal teams and portfolio companies, reviewing proposed architectures against Flagship IT guardrails.
• Work with the Pioneering Intelligence (PI) team and portfolio companies on AI/ML capabilities, including model training infrastructure, feature stores, vector databases, and LLM deployment patterns.
• Align with the Data Architecture & Engineering team on data platform design, pipeline standards, and lakehouse infrastructure.
• Work with Lab Systems and Research Systems so cloud infrastructure meets the integration and performance needs of ELN platforms, LIMS, scientific instruments, and research applications.
• Work with IT Security and Compliance to keep cloud security strong, covering SOC 2, GxP, and HIPAA-relevant requirements as the portfolio matures.
• Meet with portfolio company CTOs and heads of data science and bioinformatics to learn their scientific infrastructure needs and keep Scientific Cloud ahead of demand.

Requirements

• 10+ years in cloud engineering, infrastructure engineering, or DevOps, with at least 3 years in technical leadership or engineering management.
• Deep AWS experience, or Azure experience, or both, including compute, storage, networking, IAM, and managed data and ML services.
• Proven experience designing and running infrastructure for scientific or biotech workloads such as genomics pipelines, HPC, or -omics data processing, or equivalent work.
• Strong skill with infrastructure-as-code tools (Terraform, CDK, CloudFormation) and CI/CD automation (GitHub Actions, Jenkins, or similar).
• Hands-on experience with containers and orchestration (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 a degree in a field such as computational biology, bioinformatics, biochemistry, or a related area (B.S., M.S., or Ph.D.), or through direct industry work in scientific computing.
• A record of building and shipping reusable infrastructure products and reference architectures, not only one-off solutions.
• Strong communicator who can turn scientific needs into engineering specifications and explain technical strategy to non-technical stakeholders.
• Preferred: experience running a multi-tenant or portfolio model, supporting several independent teams or companies on shared infrastructure.
• Preferred: familiarity with AI/ML infrastructure, such as model training pipelines, MLflow or similar experiment tracking, SageMaker, or LLM deployment on cloud.
• Preferred: experience with data lakehouse architectures (Delta Lake, Iceberg, or equivalent) and their use with scientific data.
• Preferred: exposure to GxP, HIPAA, or SOC 2 compliance in a cloud setting.
• Preferred: prior work at a biotech incubator, a multi-portfolio organization, or a venture-backed life sciences company.

Conditions

The salary range is $172,000 to $236,500. Pay depends on qualifications, skills, competencies, and experience.

Candidates who meet some but not all of the listed qualifications are encouraged to apply, as the company works to build diverse and inclusive teams.

Benefits

• Healthcare coverage.
• An annual incentive program.
• Retirement benefits.
• A broad range of other benefits.
• Pay and benefits details are a good-faith estimate as of the posting date and may change in the future.