Remote Senior Healthcare ML Engineer – NLP & Data Discovery

Ellkay, Llc; United States; Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Medical, Dental, and Vision benefits

Employer-paid Life and LTD

401k w/ matching – once eligibility is

Work/life balance

Generous FTO

Remote work options

Employee Discounts

Gym membership / Exercise class stipen

On site in HQ Free daily lunches

Job summary

ELLKAY is seeking an accomplished ML/NLP Engineer to develop applied capabilities for understanding healthcare data, extracting content, and retrieving archive evidence. You will collaborate with clinical analysts and DME teams to define labels, train models, and preserve clinical meaning across production-ready solutions.

You will own domain-model quality, prototype search, and experiments from problem definition through deployment, balancing rules, embeddings, and LLMs while ensuring source

Qualifications

• 5+ years in applied ML/NLP or equivalent production engineering.
• Strong Python and SQL, applied statistics, experimental design, supervised learning, information retrieval, and NLP using modern ML frameworks.
• Hands-on experience with healthcare datasets, HL7 v2 or FHIR, and clinical terminology.
• Experience curating labels, building representative evaluation datasets, analyzing error severity, and preventing leakage across customers or source families.
• Ability to combine clinical rules, embeddings, trained models, and LLMs, and measure confidence and abstention.
• Ability to work directly with clinical SMEs and communicate model behavior to product and engineering teams.

Responsibilities

• Develop schema and document discovery, clinical entity extraction, semantic classification, terminology recommendations, and data-quality anomaly detection.
• Define clinical relevance labels and benchmarks for source-to-ELLKAY's canonical data model and FHIR mappings, archive retrieval, and interoperability recommendations.
• Combine domain rules, embeddings, supervised models, and LLMs. Preserve patient context, units, dates, negation, and source references.
• Prototype archive search and cited answers. Specify abstention and SME review when evidence is incomplete or clinical meaning is ambiguous.
• Design pilot experiments and analyze reviewer acceptance, error severity, task time, and performance across source-system families.
• Train and adapt models where labeled data and evaluation justify it. Compare against simple baselines, and retain source evidence for extracted facts and recommendations.

Skills

5+ years in applied ML/NLP

Python

SQL

Statistics

Experimental design

Supervised learning

NLP

Information retrieval

Healthcare datasets

HL7/FHIR

Clinical terminology

PyTorch

scikit-learn

Transformers

Education

Relevant degree or equivalent practical experience

Tools

PyTorch

scikit-learn

Transformers

Job description

ELLKAY is seeking an accomplished ML/NLP Engineer to develop applied capabilities for understanding healthcare data, extracting content, and retrieving archive evidence. You will collaborate with clinical analysts and DME teams to define labels, train models, and preserve clinical meaning across production-ready solutions.

You will own domain-model quality, prototype search, and experiments from problem definition through deployment, balancing rules, embeddings, and LLMs while ensuring source