Applied Research Scientist, Clinical AI

Job not on LinkedIn

🕒 July 21

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧪 Clinical Research

👻 Ghost score 13%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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RhythmScience Inc.

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2019

🏥 Healthcare

☁️ SaaS

🤝 B2B

Healthcare • SaaS • B2B

RhythmScience Inc. is a cardiac data management company that provides Rhythm360®, an integrated SaaS platform that ingests and normalizes cardiac and physiologic data from implantable and external devices to generate actionable clinical insights and unified workflows. The company augments its platform with a clinically trained patient-care and engagement team that supports remote monitoring, alert management, patient compliance, and care pathways for hypertension, heart failure, and rhythm management. RhythmScience partners with cardiology practices and health systems to simplify device data, enable remote cardiology, and improve patient outreach and outcomes.

📋 Description

• Define clinical “correctness” for ML outputs and catch critical clinical errors • Take models completely from experimental notebooks to live, working services integrated with our RS360 backend application • Develop cohort and outcome analytics from messy, patient-level monitoring data • Establish historical backtesting against human-written notes to build trust with internal engineering, product, and clinical teams • Handle Protected Health Information (PHI) safely

🎯 Requirements

• Advanced proficiency in Python, PyTorch (or equivalent framework) and SQL • Demonstrated experience building and deploying clinical or biomedical ML models (using EHR, time-series, sensor, or imaging data) • Deep comfort dealing with real-world, messy, sparse or weakly labeled device data • Strong grasp of ML evaluation methodologies, including data leakage prevention and customized metric design • Exceptional written communication skills to thrive and exert technical influence across our remote, asynchronous team • Advanced degree in related field.

🏖️ Benefits

• Bonus • Equity ownership opportunity • Benefits.