Axle Informatics

Data Scientist

• Design and implement AI/NLP methods for biomedical literature mining and structured protocol knowledge extraction.
• Develop benchmark datasets, annotation guidelines, and evaluation pipelines for scientific information extraction.
• Build and evaluate RAG, in-context learning, fine-tuning, graph matching, entity normalization, and KG query workflows.
• Analyze extraction errors, model behavior, retrieval failures, grounding quality, and biological ambiguity.
• Collaborate with software engineers to integrate research methods into usable tools and reproducible pipelines.
• Collaborate with organoid biologists and domain experts to translate biological protocol knowledge into computable representations.
• Prepare manuscripts, conference abstracts, technical reports, design documents, and open-source research artifacts.
• Help define research milestones, evaluation criteria, and publication strategy for protocol intelligence work.

Data Science Fellow – AI, NLP

AI Engineer

• Architect, develop, and maintain AI-powered applications in Python, including conversational assistants, agentic tools, and retrieval-augmented generation (RAG) systems.
• Design and optimize retrieval pipelines: embedding generation, vector search, hybrid search, and reranking against large domain-specific corpora.
• Build document ingestion and parsing pipelines that extract structured content (text, tables, figures, citations) from scientific and technical documents for downstream use by AI systems.
• Integrate structured knowledge — ontologies, controlled vocabularies, and knowledge graphs — with unstructured text to produce evidence-grounded, provenance-aware AI outputs.
• Develop and operate shared platform services that accelerate delivery of new AI applications: model gateways, agent-tool integrations, authentication, API key management, and observability.
• Lead technical design decisions and establish best practices for code quality, testing, evaluation, and deployment of AI systems.
• Provision, deploy, monitor, and troubleshoot services on Kubernetes and cloud infrastructure, including container builds, manifests, and CI/CD pipelines.
• Design and maintain relational and vector databases, including schema design, indexing, migrations, and incremental data updates.
• Define and track quality metrics for AI systems (retrieval quality, answer faithfulness, latency, cost) and drive measurable improvements over time.
• Engage directly with non-technical clients to gather requirements, clarify goals, manage expectations, and validate that delivered solutions align with their objectives.
• Translate high-level client and mission needs into actionable technical specifications and development plans.
• Mentor junior engineers and contribute to a culture of technical excellence.
• Participate in and help facilitate standups, sprint planning, and cross-team review meetings across multiple project streams.

Data Science Fellow – AI, NLP

AI Engineer II