2 Clinical Emr Jobs

Team: Product & Engineering (Data/AI) Reports to: Head of Product (dotted line to Head of Engineering) Type: Full-time Role Purpose Build, deploy, and maintain the AI/ML stack powering our EMRclinical NLP/LLM, decision support, and voice scribing. Own end-to-end data engineering, model training, and MLOps with healthcare compliance baked in (PDPA, HIPAA, ISO 27799). What Youll Do Data Platform & Pipelines Architect and operate pipelines for structured/unstructured clinical data (EHR notes, HL7 v2, FHIR, audio). Build/maintain the feature store for clinical AI (labs, meds, allergies, vitals, orders) with lineage & versioning. Implement PHI de-identification/re-identification, KMS-backed encryption, DUAs, and access controls. Clinical NER & Code Mapping (core accountability) Own the extraction + normalization stack for: problems/diagnoses, symptoms/findings, medications ( with attributes ), labs, orders, allergies. Ship a hybrid extractor (transformer NER + rules) with assertion (present/absent/etc.) and temporality . Build a medication attribute parser (dose, unit/UCUM, route, frequency, duration, PRN, instructions). Implement a two-stage entity linker (candidate gen via lexicon/vector search + cross-encoder rerank) to SNOMED CT, RxNorm, LOINC ; manage crosswalks to ICD-10/CPT and local catalogs. Operate ontology ops : version pinning, diffs, UMLS/SNOMED licensing, regression tests per ontology release. Enforce safety guards (drugallergy, duplicate therapy, dose range) and confidence-driven UI disambiguation. Modeling, LLMs & Scribing Build RAG/LLM pipelines for summarization, CDS, and scribe workflows (prompting, tool use, retrieval, guardrails). Integrate ASR + diarization with streaming partials/hotwords for clinical terms and names. MLOps, Reliability & Cost Stand up MLOps: model registry, experiment tracking, CI/CD, canary/shadow deploys, drift & safety monitoring, blue/green rollbacks. Meet SLOs: p95 speechdraft less than 2.0s , ASR partial updates every 300500ms , 99.9% uptime, rollback less than 5 min . Optimize inference (Triton/ONNX Runtime, quantization/distillation, caching) and track cost per encounter . EHR Integration & APIs Ship SMART on FHIR apps and CDS Hooks; design gRPC/REST services; run Kafka/PubSub with idempotent consumers. Security, Privacy & Compliance PHI-safe prompts/logs, prompt-injection & data-exfiltration guards, constrained tool allowlists. Audit trails exportable for clinical review & compliance. Collaboration Partner with Product & Clinical to encode guidelines/rules alongside ML. Mentor engineers; uphold code quality, reviews, and on-call. Required Qualifications 4+ years Data/ML Engineering (healthcare strongly preferred). Expert: Python , SQL , PyTorch/TensorFlow , Hugging Face . Deep NLP/LLM (transformers, RAG, prompt engineering, guardrails). Standards: FHIR/HL7 , SNOMED CT, ICD-10, RxNorm , LOINC , CPT; UMLS familiarity. MLOps (MLflow/Kubeflow/Vertex/SageMaker), containerized inference, CI/CD. Privacy/security in regulated environments (PDPA/HIPAA/ISO 27799). Nice to Have ASR/diarization (Whisper, Vosk, Kaldi), ONNX/TensorRT, Triton; gRPC/WebRTC streaming. GPU scheduling, vector DBs, OpenTelemetry, Terraform/IaC. Success Metrics (you own) NER micro-F1 0.92 (per-type 0.88 ). Linking top-1: SNOMED/RxNorm/LOINC 0.95 (top-5 0.99 ). Med attributes exact-match 0.93 ; UCUM unit validity 0.99 . Safety : drugallergy recall greater than 99% , precision greater than 95% . Latency/Reliability : p95 speechdraft less than 2.0s ; streaming extraction p95 less than 200ms /chunk; 99.9% uptime. ASR clinical WER 12% ; partial stability 0.90 . Cost per encounter within target.

Team: Product & Engineering (Data/AI) Reports to: Head of Product (dotted line to Head of Engineering) Type: Full-time Role Purpose Build, deploy, and maintain the AI/ML stack powering our EMRclinical NLP/LLM, decision support, and voice scribing. Own end-to-end data engineering, model training, and MLOps with healthcare compliance baked in (PDPA, HIPAA, ISO 27799). What Youll Do Data Platform & Pipelines Architect and operate pipelines for structured/unstructured clinical data (EHR notes, HL7 v2, FHIR, audio). Build/maintain the feature store for clinical AI (labs, meds, allergies, vitals, orders) with lineage & versioning. Implement PHI de-identification/re-identification, KMS-backed encryption, DUAs, and access controls. Clinical NER & Code Mapping (core accountability) Own the extraction + normalization stack for: problems/diagnoses, symptoms/findings, medications ( with attributes ), labs, orders, allergies. Ship a hybrid extractor (transformer NER + rules) with assertion (present/absent/etc.) and temporality . Build a medication attribute parser (dose, unit/UCUM, route, frequency, duration, PRN, instructions). Implement a two-stage entity linker (candidate gen via lexicon/vector search + cross-encoder rerank) to SNOMED CT, RxNorm, LOINC ; manage crosswalks to ICD-10/CPT and local catalogs. Operate ontology ops : version pinning, diffs, UMLS/SNOMED licensing, regression tests per ontology release. Enforce safety guards (drugallergy, duplicate therapy, dose range) and confidence-driven UI disambiguation. Modeling, LLMs & Scribing Build RAG/LLM pipelines for summarization, CDS, and scribe workflows (prompting, tool use, retrieval, guardrails). Integrate ASR + diarization with streaming partials/hotwords for clinical terms and names. MLOps, Reliability & Cost Stand up MLOps: model registry, experiment tracking, CI/CD, canary/shadow deploys, drift & safety monitoring, blue/green rollbacks. Meet SLOs: p95 speechdraft less than 2.0s , ASR partial updates every 300500ms , 99.9% uptime, rollback less than5 min . Optimize inference (Triton/ONNX Runtime, quantization/distillation, caching) and track cost per encounter . EHR Integration & APIs Ship SMART on FHIR apps and CDS Hooks; design gRPC/REST services; run Kafka/PubSub with idempotent consumers. Security, Privacy & Compliance PHI-safe prompts/logs, prompt-injection & data-exfiltration guards, constrained tool allowlists. Audit trails exportable for clinical review & compliance. Collaboration Partner with Product & Clinical to encode guidelines/rules alongside ML. Mentor engineers; uphold code quality, reviews, and on-call. Required Qualifications 4+ years Data/ML Engineering (healthcare strongly preferred). Expert: Python , SQL , PyTorch/TensorFlow , Hugging Face . Deep NLP/LLM (transformers, RAG, prompt engineering, guardrails). Standards: FHIR/HL7 , SNOMED CT, ICD-10, RxNorm , LOINC , CPT; UMLS familiarity. MLOps (MLflow/Kubeflow/Vertex/SageMaker), containerized inference, CI/CD. Privacy/security in regulated environments (PDPA/HIPAA/ISO 27799). Nice to Have ASR/diarization (Whisper, Vosk, Kaldi), ONNX/TensorRT, Triton; gRPC/WebRTC streaming. GPU scheduling, vector DBs, OpenTelemetry, Terraform/IaC.