Applied AI Scientist, Clinical AI Agents

Job not on LinkedIn

🕒 June 2

🗽 New York – Remote

🗽 New York – Remote

💵 $140k - $300k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧠 AI Research Scientist

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Phare Health

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2023

🤖 Artificial Intelligence

☁️ SaaS

🤝 B2B

💰 $3.1M Seed Round - Phare Health on 2023-11

Artificial Intelligence • SaaS • B2B

Phare Health is an AI-driven healthcare technology company that provides a clinical reasoning engine and SaaS platform to improve medical coding, clinical documentation improvement (CDI), and revenue cycle management. The platform reads the complete EHR, cross-references guidelines and local policy, and generates submission-ready code lists, query suggestions, and explainable evidence trails to catch and correct errors before billing. Phare Health offers provider toolkits (Analytics, Audit, Autonomous), integrates with EHRs via FHIR, and is HIPAA, ISO27001 and SOC2 accredited; the company aims to make claims more accurate, transparent, and financially resilient for healthcare providers.

📋 Description

• Design, build, and iterate on agentic AI systems for complex healthcare workflows, including documentation, coding, denial management, appeals, and revenue cycle automation. • Develop long-horizon agent behavior across context construction, retrieval, tool use, memory, routing, verification, escalation, and human-in-the-loop review. • Define what “good” looks like for clinical agents end-to-end, translating expert workflows into specifications, rubrics, gold standards, test cases, and clinically meaningful success criteria. • Build rigorous evaluation and feedback loops using expert review, production logs, model outputs, and benchmarks to measure performance, regressions, edge cases, safety, reliability, provenance quality, and business impact. • Prototype new AI capabilities from 0 → 1, then harden them into reliable, explainable, auditable production systems with clear contracts, monitoring, evidence, rationale, and performance gates. • Partner with research and ML engineering teams on model selection, fine-tuning, reward modeling, distillation, synthetic data, post-training, and internal AI infrastructure, including instrumentation, experiment tracking, benchmarking, prompt/version management, and reproducible evaluation.

🎯 Requirements

• Bring 4+ years of software engineering, ML engineering, research engineering, or applied AI experience. • Are highly proficient in Python and comfortable building production systems with APIs, structured data, async workflows, testing, logging, and observability. • Have experience turning messy real-world workflows into structured AI problems, including classification, ranking, extraction, decisioning, LLM applications, agents, RAG, tool calling, structured outputs, prompting, or evaluation. • Have built or operated evaluation systems, benchmarks, annotation workflows, experiment tracking, or regression tests for AI systems. • Thrive in ambiguous, high-stakes domains: working with experts, debugging real-world failures, and turning model potential into reliable, correct, safe systems that work for users.

🏖️ Benefits

• Top-of-market compensation (salary + equity) • Flexible PTO • Comprehensive health benefits • 401(k) matching • Inspiring, brilliant, mission-driven teammates