Full-Time
Principal Cloud Engineer
Updated on 9/4/2026
TetraScience
201-500 employees
Cloud-native platform centralizing scientific data
Compensation Overview
$150k - $240k/yr
Boston, MA, USA
In Person
 DevOps & Infrastructure (1) |
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- At least 7 years of experience in DevOps, cloud engineering, or platform engineering roles, including at least 2 years in a senior or lead capacity.
- Deep daily coding experience programmatically managing infrastructure through Python, APIs, and infrastructure-as-code tools.
- Strong production AWS experience with EKS, ECS, EC2, VPC, Transit Gateway, ALB/NLB, Route 53, S3, EBS, EFS, IAM, KMS, Security Hub, and GuardDuty.
- Experience designing and building CI/CD pipeline infrastructure at scale using GitHub Actions, GitLab CI, or Jenkins.
- Experience with container orchestration using ECS, Docker, and Kubernetes, with knowledge of service mesh concepts.
- Scripting and automation experience with Python or Go; Bash-only experience is insufficient.
- Experience with Git-based workflows, branch strategies, and pull-request-driven infrastructure changes.
- Experience designing and operating resilient and scalable cloud solutions.
- Experience operating in a regulated or compliance-sensitive environment such as GxP, SOC 2, HIPAA, or FedRAMP.
- Own, design, build, and maintain cloud infrastructure using CloudFormation, Terraform, and custom Python glue code.
- Architect deployment pipeline infrastructure end to end, including GitHub Actions, CodeBuild, container image pipelines, code scanning, artifact registries, pre-merge integration environments, promotion gates, and automated rollback.
- Partner with engineering teams to reduce cycle time from commit to production and identify and eliminate pipeline bottlenecks.
- Instrument pipeline metrics such as build time, deployment frequency, change failure rate, and mean time to recovery.
- Build self-service capabilities so product teams are not blocked by infrastructure.
- Evolve the cloud architecture and build substantial parts of it using Python, CloudFormation, and Terraform.
- Architect and build deployment pipelines to AWS and Databricks.
- Drive engineering practices that improve the speed and safety of software delivery.
- Apply AWS Well-Architected Framework principles to cloud engineering.
- Embed security into products and pipelines through container image scanning, SAST/DAST integration, secrets management, least-privilege IAM, and compliance-as-code.
- Operate production monitoring, alerting, log aggregation, and incident response infrastructure.
- Support developer teams and contribute to a blameless postmortem culture.
- Experience with Databricks.
- AWS Solution Architect Professional certification or an equivalent certification.
- Experience with Azure or Google Cloud Platform.
- Experience with managed database services such as Aurora, RDS, Redshift, DynamoDB, or Databricks infrastructure provisioning.
- Experience with big data infrastructure, including data lake storage architectures, lakehouses, and ETL pipeline infrastructure.
- Experience with cost optimization, resource tagging strategies, and FinOps practices in AWS.
- Experience with policy-as-code using OPA/Rego, Sentinel, or AWS Service Control Policies.
TetraScience provides a cloud-based platform that collects and centralizes data from various laboratory instruments and software used in biopharmaceutical research and manufacturing. The system works by harmonizing scattered data into a consistent format, making it easier for scientists to use information for artificial intelligence and machine learning applications. Unlike many competitors, this platform is vendor-neutral and open, meaning it can connect to any piece of lab equipment regardless of the manufacturer. The company’s goal is to improve scientific outcomes by automating data management, allowing researchers to process information in seconds rather than hours.
Company Size
201-500
Company Stage
Series B
Total Funding
$117.2M
Headquarters
Boston, Massachusetts
Founded
2019
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Simplify's Take
What believers are saying
- January 2026 Matt Studney hire brings Merck credibility and enterprise transformation experience.
- Organon deployed Scientific Data Foundry in December 2025 for quality testing modernization.
- Bayer expanded its partnership in October 2025 and Takeda joined the Lighthouse program.
What critics are saying
- A $15 million February 2026 round signals capital dependence against larger Benchling and Veeva rivals.
- Heavy reliance on Bayer, Takeda, Organon, and Thermo Fisher concentrates revenue exposure.
- If platform adoption stalls, bespoke pharma data teams will bypass TetraScience by 2027.
What makes TetraScience unique
- Tetra OS unifies scientific data, workflows, and AI for biopharma R&D.
- Basel HQ in Novartis Campus embeds teams inside Europe’s densest life sciences cluster.
- Thermo Fisher, Microsoft, Databricks, Google, and NVIDIA partnerships validate platform breadth.
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Benefits
Unlimited PTO
100% company paid health, dental, & vision
Company paid life insurance
401k savings
Company paid disability insurance
Equity program
Flexible work arrangements
Growth & Insights and Company News
6 month growth
↑ 3%1 year growth
↑ 2%2 year growth
↓ -1%TetraScience appoints Matt Studney as Chief Customer Officer, signaling industry shift toward platform-based Scientific AI. News provided by. 24-Year Merck Veteran and Former SVP of R&D IT Joins TetraScience to Help Industrialize Scientific Data and AI Across Biopharma BOSTON, Jan. 15, 2026 /PRNewswire/ - TetraScience, the Scientific Data and AI company, today announced the appointment of Matt Studney as Chief Customer Officer. Studney is a seasoned business, R&D, and technology leader with more than two decades of experience operating at the intersection of science, data, and engineering. Most recently, Studney served as Senior Vice President of R&D IT and Key Partnerships at Merck, where he led large-scale modernization of scientific, laboratory, and development platforms and helped scale data, cloud, and AI capabilities across the full R&D continuum. His work enabled faster scientific decision-making, improved reproducibility, and more resilient digital foundations for over 18,000 scientists and researchers worldwide. Studney's decision to join TetraScience reflects a broader inflection point in the biopharma industry. As scientific complexity accelerates and AI becomes central to competitive advantage, platform-based approaches to scientific data and AI are increasingly replacing bespoke, project-driven solutions across discovery, development and manufacturing. "Matt has lived firsthand the limits of artisanal approaches to scientific data and AI," said Patrick Grady, Co-Founder and CEO of TetraScience. "His move to TetraScience signals that the center of gravity is shifting - from bespoke internal efforts toward shared platforms purpose-built to make scientific intelligence durable, cumulative, and scalable." "Matt is a world-class operational leader with unparalleled credibility and relationships within the pharmaceutical industry, and his appointment represents a safe and trusted choice for pharmaceutical companies looking to partner with TetraScience on their scientific data and AI transformation journeys," added Grady. At Merck, Studney designed, established and governed strategic partnerships with AWS, Accenture, Veeva, NVIDIA, BCG X, and QuantumBlack, while overseeing modernization across lab, clinical, and manufacturing technologies. His initiatives helped reduce discovery cycle times by 33%, accelerate regulatory submissions by up to four weeks, and deliver more than $100 million in savings within the first six months of a multi-year optimization program. "Over the course of my career in one of the world's most complex pharmaceutical organizations, I've seen firsthand what works - and what breaks - when you try to scale scientific intelligence inside global pharma," said Studney. "The AI era makes clear that true transformation now requires a fundamentally new architectural foundation. Scientific intelligence cannot scale on fragmented data or bespoke workflows. TetraScience has built the platform needed to industrialize scientific data and make learning cumulative across the enterprise. Patrick's long-standing vision for Scientific AI, combined with the company's deep technical and scientific capabilities, makes clear that TetraScience is the natural steward of this next phase of the industry." Studney is a recognized industry voice across global technology and biopharma leadership forums, advising organizations on AI enablement, platform strategy, and partnership governance. At TetraScience, he will partner closely with biopharma customers to help them move beyond project-by-project modernization toward a shared scientific data and AI platform, working side-by-side through the organizational and operational change this transition requires. Through TetraScience's Sciborg model, his mandate is to translate platform architecture into durable adoption and measurable scientific, operational, and economic outcomes across discovery, development, and manufacturing. About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. Tetra OS integrates the Scientific Data Foundry, Scientific Use Case Factory, Tetra AI, and Sciborgs into a single, AI-native platform. Together, these capabilities turn fragmented scientific data and workflows into governed, reusable, and compounding intelligence across discovery, development, and manufacturing. TetraScience is trusted by leading biopharma organizations and ecosystem partners including NVIDIA, Databricks, Snowflake, Google, and Microsoft. For more information, visit tetrascience.com.
BOSTON, Aug. 13, 2025 /PRNewswire/ - TetraScience, the Scientific Data and AI Cloud company, today announced the launch of Tetra Workflows, a comprehensive solution that fundamentally transforms how laboratories manage and automate scientific data workflows at scale.
TetraScience collaborates with Microsoft to advance Scientific AI at scale.
What You Should Know: – TetraScience, a provider of scientific data cloud solutions announced a strategic collaboration with Microsoft to accelerate the adoption of artificial intelligence (AI) in the biopharmaceutical industry. – The strategic partnership combines TetraScience’s Scientific Data and AI Cloud with the power and security of Microsoft Azure, creating a robust platform for scientific organizations to extract valuable insights from their complex experimental data.Explosion of Scientific Data ChallengesThe biopharmaceutical industry is facing a critical challenge: the explosion of scientific data. While AI holds immense promise for accelerating drug discovery and development, much of this data remains trapped in proprietary formats and scattered across disparate systems. This hinders the effective training and deployment of AI models, limiting the potential for breakthroughs.Harmonizing Scientific Data and Empowering AI WorkflowsThe TetraScience and Microsoft collaboration addresses this challenge head-on. By providing a comprehensive solution that encompasses massive computational power, advanced AI models, sophisticated scientific data ontologies, and deep scientific expertise, the partnership empowers organizations to overcome data silos and unlock the full potential of AI.TetraScience’s Scientific Data and AI Cloud is purpose-built to replatform and engineer scientific data into powerful data models and domain-specific use cases. This harmonizes data from hundreds of scientific instruments and vendor formats, ensuring that experimental context is preserved for multimodal analytics and AI model training.Microsoft Azure provides the enterprise-grade infrastructure and computational backbone for demanding scientific workloads, including real-time analytics and large-scale AI applications. This ensures that researchers have the resources they need to train and deploy sophisticated AI models.Preliminary Collaboration ResultsThis collaboration is already delivering tangible results
At AI Summit Japan, TetraScience, a company that engineers AI-native scientific datasets, announced a collaboration with NVIDIA to industrialize the production of scientific AI use cases to accelerate and improve workflows across the life sciences value chain.