Lead Software Engineer - Search Platform

TetraScience

3d • Remote

About The Position

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.

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.

Requirements

• 10+ years of backend or platform engineering experience building distributed, production grade systems.
• Hands-on experience with search technologies such as Elasticsearch/OpenSearch, Lucene, or vector databases not just deployment, but custom configuration, relevance tuning, and performance optimization at scale.
• Strong understanding of semantic and hybrid retrieval: embeddings, transformer models, vector similarity, ranking logic, relevance tuning, and how to blend them with classical keyword search.
• Expert-level coding skills in TypeScript and Python building robust APIs and backend services.
• Proven ability to build and operate search infrastructure on cloud platforms (AWS preferred), including containerization, CI/CD, observability, and capacity planning.
• Familiarity with scientific or unstructured data processing, such as documents, tables, analytical results, or experimental datasets.
• Excellent communication and collaboration skills comfortable working alongside scientists, AI researchers, and product teams.
• Strong problem solving skills, while being Comfortable navigating ambiguity translating loosely defined scientific workflows and user needs into well-engineered search systems.

Nice To Haves

• Exposure to NLP, LLMs, embedding generation, or retrieval-augmented workflows.
• Experience with vector databases / embeddings stores (e.g., OpenSearch) to support semantic search and RAG.
• Contributions to open-source search projects (Apache Lucene, Solr, OpenSearch, or similar) or active involvement in the search engineering community.
• Experience with cheminformatics tools and libraries (e.g., RDKit), including molecular fingerprints, similarity metrics, or substructure search.
• Prior experience implementing chemical search systems, such as SMILES parsing, normalization, or chemical indexing.
• Experience with entity resolution, knowledge graphs, or NLP pipelines that enrich search corpora.
• Experience with large-scale data platforms such as Databricks, Lakehouse architectures, or distributed indexing systems.

Responsibilities

• 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.

Benefits

• 100% employer-paid benefits for all eligible employees and immediate family members
• Unlimited paid time off (PTO)
• 401K
• Flexible working arrangements - Remote work
• Company paid Life Insurance, LTD/STD
• A culture of continuous improvement where you can grow your career and get coaching

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed