Lead Platform Engineer - Search Platform

TetraScience

1d • Remote

About The Position

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.

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
• Strong understanding of semantic search concepts embeddings, transformers, similarity scoring, ranking logic, relevance tuning, hybrid retrieval.
• Expert-level coding skills in TypeScript and Python building robust APIs and backend services.
• Experience building and operating microservices or search infrastructure on cloud platforms (AWS preferred), including containerization, CI/CD, observability, and performance tuning.
• Familiarity with scientific or unstructured data processing, such as documents, tables, analytical results, or experimental datasets.
• Strong problem solving skills, with the ability to navigate ambiguous scientific workflows and translate them into engineered systems.
• Excellent communication and collaboration skills comfortable working alongside scientists, AI researchers, and product teams.
• Exposure to NLP, LLMs, embedding generation, or retrieval-augmented workflows.
• Experience with large-scale data platforms such as Databricks, Lakehouse architectures, or distributed indexing systems.

Nice To Haves

• 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.
• Knowledge of vector databases / embeddings stores (e.g., OpenSearch) to support semantic search and RAG.

Responsibilities

• 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.

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: Mid Level
• Education Level: No Education Listed
• Number of Employees: 101-250 employees