Note: The job is a remote job and is open to candidates in USA. ELLKAY is hiring a Senior Applied ML Engineer to develop and productionize machine learning and NLP capabilities for understanding healthcare data, discovering migration scope, extracting document content, and retrieving archive evidence. The role partners with clinical analysts and data, product, and engineering teams to define labels, evaluate models, and preserve clinical meaning and evidence.
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
- 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
Skills
- 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
Benefits
- 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
- Working with talented, collaborative, and friendly people who love what they do
- Professional growth within
- Innovation environment
- On site in HQ Free daily lunches
- Hybrid workplace, offering employees flexibility in how they structure their time between in-office and remote work
- For those who do not live within 40 miles of one of our offices, remote work may be considered for candidates whose skills and experience strongly align with the role; remote work is also open for specific positions and specialized skill sets
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