Senior Applied ML Engineer - Healthcare NLP (Remote)

Ellkay, Llc; New Jersey; Hybrid; USD 160,000 - 210,000; Full time

Benefits offered by this job

Remote work options

Medical, Dental, and Vision benefits

Employer-paid Life and LTD

401k w/ matching

Work/life balance

Generous PTO

Gym membership / stipends

Employee discounts

Job summary

ELLKAY LLC in New Jersey is seeking an experienced ML/NLP Engineer to develop capabilities that understand healthcare data, extract content, and retrieve archive evidence. You will collaborate with clinical analysts and DME teams to define labels, train models, and ensure clinical meaning is preserved.

You will join a team shaping end-to-end model quality, production adoption, and evidence preservation across source families, with hybrid in-office/remote options.

Qualifications

• 5+ years in applied ML or NLP and production engineering experience.
• Strong Python and SQL, statistics, experimental design, NLP and IR.
• Experience with healthcare datasets, HL7 v2 or FHIR, clinical terminology.
• Ability to combine rules, embeddings, models, and LLMs, and prevent leakage.
• Ability to work with clinical SMEs and communicate model behavior.

Responsibilities

• Develop schema and document discovery, clinical entity extraction, semantic classification, data-quality checks.
• Define clinical relevance labels and benchmarks for data models and mappings.
• Combine domain rules, embeddings, supervised models, and LLMs.
• Prototype archive search and establish abstention review when evidence is incomplete.
• Design pilot experiments and analyze performance across source-system families.
• Train and adapt models with labeled data and preserve source evidence.

Skills

Applied ML

NLP

Python

SQL

Statistics

Experiment design

Information retrieval

NLP frameworks

Education

Relevant degree or equivalent experience

Tools

PyTorch

scikit-learn

Transformer models

Job description

ELLKAY LLC in New Jersey is seeking an experienced ML/NLP Engineer to develop capabilities that understand healthcare data, extract content, and retrieve archive evidence. You will collaborate with clinical analysts and DME teams to define labels, train models, and ensure clinical meaning is preserved.

You will join a team shaping end-to-end model quality, production adoption, and evidence preservation across source families, with hybrid in-office/remote options.