Lead Software Engineer - Search Platform

About TetraScience

TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world’s leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience’s growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft.

In connection with your candidacy, you will be asked to carefully review “The Tetra Way,” authored by our CEO, Patrick Grady; it is impossible to overstate the importance of this document, and you should take it literally as you decide whether our mission, culture, and expectations are right for you.

The Role

We're building a search platform that helps scientists find answers across billions of data points from chemical structures and assay results to unstructured lab documents and instrument data. We're looking for a Lead/Principal Platform Engineer to lead that effort.

You'll own the full search stack: indexing and scoring, query understanding and rewriting, retrieval pipelines, and the infrastructure underneath it all. You should be able to fluently apply the state-of-the-art in classical search, custom analyzers, index design alongside newer methods for semantic and hybrid retrieval. You'll go well beyond out-of-the-box OpenSearch to build custom ranking logic, relevance tuning, and scoring models that surface the right result from massive, heterogeneous scientific datasets.

This is a hands-on technical leadership role. As the technical leader of the Search Platform team, you'll write code, architect systems, mentor engineers, and shape the roadmap for search capabilities and platform evolution. You'll often operate in ambiguous territory translating loosely defined scientific workflows into well-architected search systems where the "right answer" isn't always obvious and requirements evolve as scientists discover new ways to use the platform. You'll collaborate daily 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.

The domain is bio-pharma R&D, and the data types are fascinating molecular structures (SMILES), experimental datasets, knowledge graphs linking compounds to targets and assays. You don't need to know cheminformatics today, but you should be excited to apply deep search expertise to novel and complex data types.

If you've spent your career building scalable search systems and want to do it at the intersection of AI and scientific discovery, we'd love to talk.

What You will Do

• Architect a full-stack Search Platform across all layers of indexing and scoring,  query understanding, rewriting and federation, and extensible search experiences.
• Continuously improve search quality through evaluation metrics such as precision@K, recall@K, MRR, and relevance testing with real scientific use cases.
• 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.
• Lead by example and write code, review designs, and set the standard for engineering quality on the Search Platform team. Mentor engineers and help grow the team's search and distributed systems expertise.
• Contribute to architectural decisions, technical strategy, and platform-wide improvements to accelerate scientific insight generation.
• 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.
• Ensure security, compliance, and tenant isolation as part of operating search services in enterprise bio-pharma environments.
• 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.