Sr Biomedical NLP Engineer

We are a forward-thinking healthcare technology company dedicated to empowering individuals to take control of their health. Our mission is to create a consumer-facing portable health record platform that prioritizes user agency, accessibility, and security. We are seeking a Principal MediaWiki Architect to lead the development of this transformative platform.

Job type: Full-time

Job Summary

We are seeking a Senior Biomedical NLP Engineer to lead the development of AI-driven clinical information extraction from medical records. This role requires expertise in clinical informatics, natural language processing (NLP), and large language models (LLMs), with a focus on extracting, normalizing, and structuring medical concepts (diagnoses, medications, procedures, labs, vitals, and immunizations) into standardized terminologies like SNOMED CT, ICD, LOINC, RxNorm, CVX and others.

As the field of AI and biomedical NLP evolves rapidly, this individual will perform state-of-the-art assessments, evaluating emerging LLM-based and traditional NLP approaches, conducting build vs. buy analyses, and integrating the best methodologies into a scalable enrichment pipeline. The ideal candidate has a deep technical background, can rapidly adapt to new research, and is experienced in developing production-ready NLP systems for healthcare using state of the art models/LLMs, etc. adapted to company specific data/product pipelines.

Additionally, this role requires strong project management skills to ensure efforts remain focused on high-impact initiatives. The candidate will be responsible for prioritizing tasks, aligning AI development with business objectives, managing timelines, and ensuring effective cross-functional collaboration.

Key Responsibilities

NLP & AI Development for Clinical Data Extraction

• Design and implement biomedical NLP models for extracting and structuring clinical concepts from unstructured medical records.
• Develop methods to normalize extracted concepts to standard medical ontologies (e.g., SNOMED CT, ICD-10, RxNorm, LOINC, CPT).
• Optimize named entity recognition (NER), entity linking, relation extraction, and concept normalization in medical text.
• Stay current with LLM advancements (e.g., GPT, BioBERT, MedPaLM) and evaluate their applicability in clinical NLP.
• Benchmark LLMs vs. rule-based and classical ML/NLP models for various extraction tasks, performing rigorous performance evaluations.

Project Management & Strategic Focus

• Define, prioritize, and manage NLP development efforts, ensuring alignment with company objectives and product needs.
• Develop and maintain project roadmaps, balancing short-term deliverables with long-term AI strategy.
• Ensure efforts are directed at solving high-value clinical and business problems, rather than pursuing purely academic research.
• Establish clear metrics for success, including precision/recall benchmarks, real-world validation, and adoption KPIs.
• Foster cross-functional collaboration with data engineers, product managers, regulatory teams, and healthcare experts.
• Proactively identify roadblocks, mitigate risks, and adjust strategies to keep projects on track.
• Communicate technical progress and challenges effectively to non-technical stakeholders (executives, clinical experts, customers).

State-of-the-Art Evaluation & Build-vs-Buy Analysis

• Continuously monitor research and industry advancements in biomedical NLP, LLMs, and medical AI.
• Evaluate emerging third-party AI models, APIs, and commercial solutions, comparing them against in-house models for accuracy, scalability, and cost-effectiveness.
• Conduct build vs. buy analyses, ensuring that selected solutions meet efficiency, interpretability, and compliance requirements.

End-to-End Clinical NLP Pipeline Development

• Architect an end-to-end pipeline for extracting, processing, and enriching clinical data.
• Design systems that integrate with EHRs, FHIR APIs, claims data, and health information exchanges.
• Optimize data ingestion, model inference, and knowledge graph construction for structured outputs.
• Ensure compliance with HIPAA, GDPR, and other healthcare data privacy regulations.

Model Deployment, Evaluation, & Continuous Improvement

• Implement model monitoring, validation, and bias detection strategies to maintain accuracy over time.
• Utilize MLOps best practices for scalable deployment, retraining, and real-time inference.
• Develop feedback loops to improve extraction accuracy based on user interactions and external validation.

Required Qualifications

• Advanced degree (MS/PhD) in Biomedical Informatics, AI, Machine Learning, NLP, Computational Linguistics, or a related field.
• 5 years experience in biomedical NLP, clinical text processing, and LLMs for healthcare.
• Strong expertise in Python, TensorFlow/PyTorch, Hugging Face Transformers, spaCy, and Scikit-learn.
• Experience with FHIR, OMOP CDM, and clinical terminologies (SNOMED CT, LOINC, RxNorm, etc.).
• Knowledge of statistical NLP, transformers, prompt engineering, retrieval-augmented generation (RAG), and fine-tuning LLMs for domain adaptation.
• Proven ability to evaluate, compare, and integrate new AI/NLP research into production environments.
• Strong analytical skills for conducting quantitative benchmarking of NLP models and AI services.
• Experience with cloud AI/ML infrastructure (AWS, GCP, Azure) and MLOps pipelines.
• Demonstrated experience leading AI/ML projects, setting priorities, managing deliverables and delivering product in a fast-paced environment.

Preferred Qualifications

• Prior work in health tech, EHR systems, or clinical decision support.
• Familiarity with vector databases (e.g., FAISS, Weaviate) for medical knowledge retrieval.
• Experience with LLM fine-tuning techniques like LoRA, adapters, and RLHF for medical use cases.
• Background in healthcare regulatory compliance (HIPAA, GDPR, FDA AI/ML guidelines).
• Experience leading cross-functional teams or mentoring junior engineers.

Why Join Us?

• Work on cutting-edge biomedical NLP and AI applied to real-world clinical data.
• Dominate the future of AI-driven personal health records and global medical interoperability.
• Collaborate with top-tier AI researchers, data scientists, and healthcare innovators.

Competitive salary, equity options, and continuous learning opportunities in a fast-growing health AI company.

How to Apply:

Please submit your resume, a cover letter detailing your experience to das@selfii.com.