Senior Applied ML Engineer at ELLKAY

Job description

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. Essential Duties & 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. Collaboration and ownership 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. Qualifications: Required qualifications: • 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. Preferred qualifications: 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. Expected outcomes • 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 performance across two source families and preserve provenance for all exposed clinical facts. • Quality, performance, and business-impact targets will be calibrated to the selected use cases and their initial baselines. Delivery depends on the corresponding platform capabilities and approved customer pilot scope. Benefits: ELLKAY offers a comprehensive and competitive benefit package that starts day one! Including: • Medical, Dental, and Vision benefits • Employer-paid Life and LTD • 401k w/ matching – once eligibility is met • Work/life balance • Paid Volunteer Program • Flexible working hours • Generous FTO • Remote work options • Employee Discounts • Parental Leave • Gym membership / Exercise class stipends Our awesome culture includes: • Working with talented, collaborative, and friendly people who love what they do • Professional growth within • Innovation environment • On site in HQ Free daily lunches Awards: • 2025 Top Workplaces Employee Appreciation • 2025 Top Workplaces Employee Well-Being • 2025 Top Workplaces Professional Development • 2025 Top Workplaces Leadership • 2026 USA Today Top Workplaces • 2026 NJ Top Workplaces Additional information At ELLKAY, we are committed to operating as a hybrid workplace, offering employees flexibility in how they structure their time between in-office and remote work. We recognize the significance of fostering connections, collaboration, and creativity within our office culture and its positive impact on our business. Our philosophy of operating as a hybrid workplace underscores our dedication to enabling employees to tailor work-life balance to their individual preferences. For those who do not live within 40 miles of one of our offices, we are open to considering remote work for candidates whose skills and experience strongly align with the role. While we prioritize a hybrid work environment for most roles, we understand the importance of flexibility and are open to remote work for specific positions and specialized skill sets. For more information on our company, visit www.ELLKAY.com. ELLKAY LLC is a Smoke-Free Workplace. ELLKAY, LLC provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Compensation Range: $170,000 - $210,000

Requirements

Must-haves

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.
Collaboration and ownership
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.
Employer-paid Life and LTD
Work/life balance
Paid Volunteer Program
Flexible working hours
Generous FTO
Remote work options
Employee Discounts

Nice-to-haves

Expected outcomes
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.
Quality, performance, and business-impact targets will be calibrated to the selected use cases and their initial baselines. Delivery depends on the corresponding platform capabilities and approved customer pilot scope.

Application prep

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Keywords to mirror

mlpythonsql

Platform: adp-workforce-now
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Parental Leave

Professional Development