Full-Time
Senior Software Platform Engineer
TetraScience
201-500 employees
Cloud-native platform centralizing scientific data
No salary listed
No H1B Sponsorship
Remote in USA
Remote
 DevOps & Infrastructure (1) |
|---|
- 7+ years of professional experience in software engineering and infrastructure engineering.
- Extensive experience building and maintaining AI/ML infrastructure in production, including model, deployment, and lifecycle management.
- Strong knowledge of AWS and infrastructure-as-code frameworks, ideally with Cloud Development Kit.
- Expert-level coding skills in TypeScript and Python building robust APIs and backend services.
- Production-level experience with Databricks MLFlow, including model registration, versioning, asset bundles, and model serving workflows.
- Expert level understanding of containerization (Docker), and hands on experience with CI/CD pipelines, orchestration tools (e.g., ECS) is a plus.
- Proven ability to design reliable, secure, and scalable infrastructure for both real-time and batch ML workloads.
- Ability to articulate ideas clearly, present findings persuasively, and build rapport with clients and team members.
- Strong collaboration skills and the ability to partner effectively with cross-functional teams.
- Design, implement, and maintain cloud-native platform to support AI and data workloads, with a focus on AI and data platforms such as Databricks and AWS Bedrock.
- Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics.
- Develop infrastructure-as-code using tools like Cloudformation, AWS CDK to ensure repeatable and secure deployments.
- Collaborate with AI engineers, data engineers, and platform teams to improve the performance, reliability, and cost-efficiency of AI models in production.
- Drive best practices for observability, including monitoring, alerting, and logging for AI platforms.
- Contribute to the design and evolution of our AI platform to support new ML frameworks, workflows, and data types.
- Stay current with new tools and technologies to recommend improvements to architecture and operations.
- Integrate AI models and large language models (LLMs) into production systems to enable use cases using architectures like retrieval-augmented generation (RAG).
- Familiarity with emerging LLM frameworks such as DSPy for advanced prompt orchestration and programmatic LLM pipelines.
- Understanding of LLM cost monitoring, latency optimization, and usage analytics in production environments.
- Knowledge of vector databases / embeddings stores (e.g., OpenSearch) to support semantic search and RAG.
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
Simplify's Take
What believers are saying
- Matt Studney's appointment brings $100M+ pharma ROI track record and 24-year Merck credibility.[Recent News]
- Tetra Workflows expansion into automation orchestration captures workflow gap competitors cannot address.[Recent News]
- Bayer multi-division partnership validates platform scalability across heterogeneous scientific domains beyond pharma.[Recent News]
What critics are saying
- Benchling captures 30% more biopharma adoption with faster deployment and lower costs.[Recent News]
- IDBS E-WorkBook dominates 40% of top-20 pharmas via Danaher's established sales channels.[Recent News]
- Open-source scientific schemas commoditize data standardization, enabling 70% in-house platform replacement.[Recent News]
What makes TetraScience unique
- Only AI-native platform purpose-built for scientific data across discovery, development, manufacturing.[1][4]
- Largest integrated partner ecosystem connecting 10+ top-20 pharma companies via vendor-neutral architecture.[4]
- Sciborg model operationalizes adoption through dedicated change management and measurable ROI delivery.[Recent News]
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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
↑ 0%1 year growth
↓ -5%2 year growth
↓ -6%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.