Boston, MA
Scientific data and AI cloud for life sciences R&D
TetraScience builds a vendor-neutral cloud platform that ingests, harmonizes, and transforms raw scientific data from lab instruments and informatics into AI-native datasets. It serves biopharma companies to enable analytics, ML use cases, and faster drug discovery across R&D, manufacturing, and quality. This unlocks siloed data to improve productivity and scientific outcomes.
Builds and operates scalable full-stack distributed applications using C#/.NET, TypeScript, React, and AWS. The role requires strong microservices, automated testing, CI/CD, production debugging, and observability experience.
Leads the architecture and operation of a multi-tenant AI/ML platform supporting production models, LLMs, and agents in regulated scientific environments. Requires 10+ years in distributed cloud-native systems, strong TypeScript and Python skills, production LLM/RAG experience, and technical leadership.
Own and evolve the core platform architecture for TetraScience's Scientific Data and AI cloud, spanning enterprise platform, scientific search, AI/ML Ops, developer platform, lakehouse, partner integrations, and cloud infrastructure. Requires 12+ years experience with deep ownership of authz, search, data lakes, and AI infrastructure at SaaS scale.
Senior Applied AI/ML Engineer responsible for designing, developing, training, and deploying AI/ML products focused on life sciences and drug discovery acceleration. Requires advanced degree, 10+ years AI/ML experience (or 5+ in regulated life sciences), end-to-end product ownership, and expertise in RL, fine-tuning, or LLM agents.
Own documentation systems and AI-augmented workflows for a scientific data and AI platform. Build and maintain docs-as-code pipelines, LLM-backed authoring tools, and content optimized for AI agent consumption.
Lead Platform Engineer architecting and scaling distributed cloud-native backend services, owning authorization, metadata, governance, and high-throughput event processing on AWS. Requires 10+ years building production-grade platforms and deep expertise in TypeScript, Python, and IaC.
Hands-on technical lead owning cloud infrastructure, CI/CD pipelines, and deployment automation for a multi-tenant SaaS platform. Architect and build production systems using AWS, Terraform, CloudFormation, and Python in a GxP-regulated environment.
Leads Scientific Data Engineering team to build data pipelines, schemas, and parsers for pre-clinical lab data using Python/SQL/AI. Architects solutions, mentors juniors, and delivers customer-focused data products with dashboards in React/Streamlit. Requires 8+ years experience.
Leads full-cycle sales for scientific data cloud platform targeting life sciences firms, owning technical discovery, solution architecture, POCs, RFPs, and account expansion. Requires 7+ years in scientific software sales, deep lab data expertise, and advanced life sciences degree preferred.
Owns full sales cycle for scientific data cloud solutions targeting life sciences firms, leading technical discovery, demos, POCs, RFPs, and commercial closes while managing account expansion. Requires 7+ years in scientific software sales and deep lab data expertise.
Supports the CEO by coordinating executive engagements with biopharma, tech partners, and government; owns follow-through on alliances and initiatives; crafts science-business-policy narratives. Requires MS/PhD in life sciences plus business/policy experience, high judgment, and flexible schedule.
Leads legal strategy, risk management, and compliance for a life sciences SaaS company. Drafts/negotiates complex contracts, advises on data/IP issues, and collaborates with product/engineering teams. Requires 10-15+ years experience in pharma/biotech and SaaS legal matters, plus JD and bar membership.