Senior Applied ML Engineer

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Full Time US

3 days ago Requisition

ELLKAY started out providing connectivity solutions to laboratories and within a few years, grew to also provide data management solutions to ambulatory organizations. ELLKAY is now a trusted data management partner in five healthcare segments. ELLKAY’s solutions continue to serve laboratories and ambulatory practices and have expanded to empower hospitals and health systems, healthcare IT vendors, ambulatory practices, health plans, and other healthcare organizations with cutting-edge technologies and solutions that drive their growth and interoperability strategies.

Today, ELLKAY remains true to our core values, building strong partner relationships and offering unparalleled service and support while providing innovative, scalable solutions to the challenges our customers face in today’s data-rich world. ELLKAY's experience, customer-focused approach, and reputation for innovation, speed, and accuracy differentiate ELLKAY as a premier partner for your interoperability needs and data management strategy.

Job Description:

Develop applied ML and NLP capabilities that help ELLKAY understand healthcare data, discover migration scope, extract document content, and retrieve relevant archive evidence. The engineer will work with clinical analysts and DME teams to define labels, train and evaluate models, and preserve clinical meaning. The role owns domain-model quality and experiments from problem definition through production adoption.

• 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.

Partner with Data Platform and product engineers, clinical/data SMEs, DME/QA, and security teams. Build reusable services for ELLKAY products and preserve source evidence, approved data-use rights, and tenant isolation. Model or agent proposals follow established human approval and platform release workflows.

• 5+ years in applied ML, NLP, or equivalent production engineering experience, including model development and evaluation.
• Strong Python and SQL, applied statistics, experimental design, supervised learning, information retrieval, and NLP using modern ML frameworks.
• Hands-on experience with healthcare datasets, EHR/lab workflows, 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. A relevant degree or equivalent practical experience is acceptable.

LOINC, SNOMED CT, ICD-10, RxNorm, OCR/document extraction, clinical negation and temporal reasoning, learning-to-rank, and migration/archive experience. Familiarity with PyTorch, scikit-learn, transformer models, or equivalent tools.

• First 90 days: define the selected healthcare use case, produce an SME-reviewed benchmark with error severity labels, and establish manual/rules/model baselines.
• By six months: deliver a discovery, extraction, or retrieval pilot that meets agreed clinical quality thresholds and reports precision/recall, evidence relevance, coverage, and abstention.
• By 12 months: target 30% less analyst discovery/review time on comparable pilot tasks, with no increase in critical errors. Demonstrate…