Lead Platform Engineer - Search Platform

Who We Are

TetraScience is the Scientific Data and AI company. We are catalyzing the Scientific AI revolution by designing and industrializing AI-native scientific data sets, which we bring to life in a growing suite of next gen lab data management solutions, scientific use cases, and AI-enabled outcomes. 

TetraScience is the category leader in this vital new market, generating more revenue than all other companies in the aggregate. In the last year alone, the world’s dominant players in compute, cloud, data, and AI infrastructure have converged on TetraScience as the de facto standard, entering into co-innovation and go-to-market partnerships: Latest News and Announcements | TetraScience Newsroom:

In connection with your candidacy, you will be asked to carefully review the Tetra Way letter, authored directly by Patrick Grady, our co-founder and CEO. This letter is designed to assist you in better understanding whether TetraScience is the right fit for you from a values and ethos perspective. 

It is impossible to overstate the importance of this document and you are encouraged to take it literally and reflect on whether you are aligned with our unique approach to company and team building. If you join us, you will be expected to embody its contents each day. 

The Role

We are seeking a Lead Software Engineer to help expand our scientific search platform beyond traditional keyword search and unlock new capabilities in chemical search, semantic search, and natural language search. In this role, you will work at the intersection of AI/ML, cheminformatics, knowledge representation, and distributed systems, enabling scientists to retrieve and reason over complex experimental datasets, chemical entities, assay results, and unstructured lab documents.

As a technical leader of the Search Platform team, you will guide, design and implement new search capabilities while also owning the underlying infrastructure that powers them. Lead by example for building the systems, mentor engineers, and help shape the roadmap for search capabilities and platform evolution. This includes maintaining scalable, reliable services and continuously improving the platform as we expand our search offerings. You will collaborate closely with Applied AI Scientists, platform engineers, and product teams to deliver high performance search services that drive discovery, analysis, and decision making across the bio-pharma R&D lifecycle.

If you are passionate about building scalable search systems, advancing scientific retrieval, and supporting production scale AI workloads, we’d love to talk to you.

What You will Do

• Lead by example to architect and code the next-generation scientific search engine, building a system that can reason over billions of scientific data points—from chemical structures (SMILES) to unstructured lab documents and instrument data.
• Engineer sophisticated hybrid search pipelines that blend sparse (keyword), structured (metadata), and dense (vector) retrieval. You will go beyond out-of-the-box OpenSearch to design custom ranking logic, reciprocal rank fusion, and relevance tuning that surfaces the exact "needle in the haystack" for drug discovery.
• Own and operate the Search Platform infrastructure, ensuring high availability, scalability, performance, and observability across indexing, embedding generation, and query execution.
• Develop and maintain backend services and APIs in Python and TypeScript  that power search capabilities for scientists, data engineers, and AI applications.
• Collaborate with Applied AI Scientists to integrate embeddings, transformer models, and chemical fingerprints into production search workflows.
• Architect and implement scientific entity resolution and knowledge graph pipelines to transform raw text into interconnected knowledge. You will design systems that extract and link chemical and biological entities (NER/NED) from unstructured documents, enabling the search engine to "understand" relationships between compounds, targets, and assays.
• Continuously improve search quality through evaluation metrics such as precision@K, recall@K, MRR, and relevance testing with real scientific use cases.
• Ensure security, compliance, and tenant isolation as part of operating search services in enterprise bio-pharma environments.
• Contribute to architectural decisions, technical strategy, and platform-wide improvements to accelerate scientific insight generation.