Heidi Health
Artificial Intelligence • Healthcare Insurance • Productivity
Heidi Health is an AI-powered medical scribe platform designed for clinicians in various healthcare settings. It helps decrease the time spent on administrative tasks like note-taking, insurance processing, and result finding, thereby allowing clinicians to focus more on patient care. The platform supports multiple types of healthcare professionals including psychologists, dietitians, vets, medical doctors, nurses, and others, leveraging artificial intelligence to enhance efficiency and accuracy in clinical documentation. The goal is to provide an intuitive, efficient tool that integrates seamlessly with healthcare workflows to improve the quality of care and clinician satisfaction. It emphasizes privacy and security, featuring hospital-grade standards to protect sensitive health information.
11 - 50 employees
🤖 Artificial Intelligence
⚕️ Healthcare Insurance
⚡ Productivity
Staff AI Engineer – Orchestration
Job not on LinkedIn
October 31
Heidi Health
Artificial Intelligence • Healthcare Insurance • Productivity
Heidi Health is an AI-powered medical scribe platform designed for clinicians in various healthcare settings. It helps decrease the time spent on administrative tasks like note-taking, insurance processing, and result finding, thereby allowing clinicians to focus more on patient care. The platform supports multiple types of healthcare professionals including psychologists, dietitians, vets, medical doctors, nurses, and others, leveraging artificial intelligence to enhance efficiency and accuracy in clinical documentation. The goal is to provide an intuitive, efficient tool that integrates seamlessly with healthcare workflows to improve the quality of care and clinician satisfaction. It emphasizes privacy and security, featuring hospital-grade standards to protect sensitive health information.
11 - 50 employees
🤖 Artificial Intelligence
⚕️ Healthcare Insurance
⚡ Productivity
📋 Description
• Define the end‑to‑end architecture for literature and guideline ingestion, normalization, metadata extraction, de‑duplication, and versioning. • Build hybrid search and retrieval: lexical + vector + re‑ranking, with tight latency budgets and cost controls. • Design grounding and answer synthesis that cite sources, preserve provenance, and expose confidence and abstention. • Lead model work across prompting, fine‑tuning, distillation, and tool use to improve faithfulness, coverage, and utility. • Stand up gold‑standard evaluation: offline IR metrics (nDCG, MAP, recall), factuality/faithfulness audits, and human review with adjudication. • Run online experiments at scale. Define guardrails, KPIs, and ship A/Bs to measure impact on clinician workflows. • Productionize services with observability, tracing, canaries, rollbacks, and incident playbooks. • Set data governance for medical content: access control, PHI handling, audit logs, and retention policies. • Partner with clinicians to define intents, schemas, and acceptance criteria. Convert ambiguous questions into testable specs. • Coach engineers and scientists. Raise the technical bar through design docs, reviews, and reusable components.
🎯 Requirements
• Staff‑level track record shipping search, NLP, or LLM systems that serve real users at scale. • Mastery of Python and SQL. Strong software engineering fundamentals, testing strategy, and API/service design. • Depth in modern IR/NLP: embeddings, ANN indexes, re‑rankers, retrieval‑augmented generation, and prompt/program synthesis. • Experience building data pipelines: parsing PDFs/HTML, OCR when needed, metadata extraction, and content hashing/versioning. • Familiarity with PyTorch, plus distributed training/inference patterns. • MLOps and reliability: containers, Kubernetes, feature/model registries, experiment tracking, monitoring, and alerting. • Evidence of rigorous evaluation design: offline metrics, human‑in‑the‑loop judging, power analysis for online tests. • Clear thinking on safety: hallucination controls, calibration, abstention, red‑teaming, and privacy/security by design. • Ability to lead cross‑functional initiatives and make crisp decisions with incomplete information. • Bonus: Search relevance expertise for long‑form, citation‑heavy domains. • Knowledge of biomedical ontologies and standards (e.g., SNOMED CT, UMLS, ICD, RxNorm, FHIR). • Prior work with literature and guideline corpora, de‑duplication, and document lineage tracking. • Experience with hybrid retrieval stacks (e.g., BM25 + ANN) and learned re‑rankers. • Familiarity with clinical evaluation methods, EBM hierarchies, and annotation workflows. • Strong cost‑performance tuning for LLM inference, caching, and batching in production.
🏖️ Benefits
• Flexible hybrid working environment, with 3 days in the office. • Additional paid day off for your birthday and wellness days • Special corporate rates at Anytime Fitness in Melbourne, Sydney tbc. • A generous personal development budget of $500 per annum • Learn from some of the best engineers and creatives, joining a diverse team • Become an owner, with shares (equity) in the company, if Heidi wins, we all win • The rare chance to create a global impact as you immerse yourself in one of Australia’s leading healthtech startups • If you have an impact quickly, the opportunity to fast track your startup career!
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