TetraScience offers a Scientific Data and AI Cloud designed to liberate, unify, and transform raw scientific data into AI-native data. This platform aims to enable Scientific AI by providing a…
AI CloudChromatographyData ManagementData ReplatformingGxP ComplianceLab AutomationLife SciencesScientific Datatetrascience.com
TetraScience
TetraScience offers a Scientific Data and AI Cloud designed to liberate, unify, and transform raw scientific data into AI-native data. This platform aims to enable Scientific AI by providing a…
AI CloudChromatographyData ManagementData ReplatformingGxP ComplianceLab AutomationLife SciencesScientific Datatetrascience.com
HQBoston, US
Team Size182
Open Jobs39
Total Funding$99M
Latest Fundraise5 years ago
TL;DR
Company: TetraScience — scientific data and AI cloud for life‑sciences R&D
Headquarters / Founded: Boston; founded (original) 2014; rebuild noted from 2017; current founding year reported 2019 in one source
Product: Tetra Scientific Data and AI Cloud (Tetra OS) — vendor‑agnostic, AI‑native data platform
Recent round: Series B $80M announced April 15, 2021 (co‑led by Insight Partners and Alkeon Capital)
Used by many large pharma/biotech customers (company cites percent of top pharma customers)
Customers / traction:
Company Overview
Problem Domain
Scientific R&D data management and enabling AI for discovery and research productivity in life sciences.
Founded
2019
Industry
Software Development
Tech Stack
Cloud infrastructure / integrations
Instrument and informatics integrations
Funding Track Record
Series A- 2019-10-31
8,000,000
Series A included strategic investor Waters Corporation and investors Floodgate, First Round, Underscore VC, Founder Collective
Series B- 2021-04-15
80,000,000
Announced to accelerate rollout of the open R&D Data Cloud
Investor Signal
“Investors include Insight Partners, Alkeon Capital, Waters Corporation, Floodgate, First Round Capital, Underscore VC, Founder Collective, and others”
Founders
What we do
Join the Team
Senior Software Platform Engineer
On-SiteUnited States {{REMOTE}}, US
On-Site • United States {{REMOTE}}, US
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We’re looking for a Senior AI Platform Engineer to help design, build, and scale our AI and data infrastructure
In this role, you’ll focus on architecting and maintaining cloud-based MLOps pipelines to enable scalable, reliable, and production-grade AI/ML workflows, working closely with AI engineers, data engineers, and platform teams
Your expertise in building and operating modern cloud-native infrastructure will help enable world-class AI capabilities across the organization
Design, implement, and maintain cloud-native infrastructure 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)
Benefits
Unlimited PTO
100% Company Paid Health, Dental & Vision
Company Paid Life Insurance
401k Savings
Company Paid Disability Insurance
Equity Program- Strong knowledge of AWS and infrastructure-as-code frameworks, ideally with CDK
Extensive experience building and maintaining AI/ML infrastructure in production, including model, deployment, and lifecycle management
Expert level understanding of containerization (Docker), and hands on experience with CI/CD pipelines, orchestration tools (e.g., ECS) is a plus
Production-level experience with Databricks MLFlow, including model registration, versioning, asset bundles, and model serving workflows
Expert-level coding skills in TypeScript and Python building robust APIs and backend services
7+ years of professional experience in software engineering and infrastructure engineering
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
Proven ability to design reliable, secure, and scalable infrastructure for both real-time and batch ML workloads
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
Familiarity with emerging LLM frameworks such as DSPy for advanced prompt orchestration and programmatic LLM pipelines
If you are passionate about building robust AI infrastructure, enabling rapid experimentation, and supporting production-scale AI workloads, we’d love to talk to you