TetraScience
Lead Platform Engineer- Search Platform
Vaga remota de Platform Engineering com fit claro de localização do candidato.
Publicada3 de jul. de 2026
Países elegíveis1 país aceito
Sinal de senioridadeSenior, Lead
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Estados Unidos
Resumo da vaga
Lead Platform Engineer- Search Platform
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
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.
What You will Do
- 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.
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.
- 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.
- Strong problem solving skills, while being Comfortable navigating ambiguity translating loosely defined scientific workflows and user needs into well-engineered search systems.
Valued Experience
- 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.
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
Benefits
- We are not currently providing visa sponsorship for this position.
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