Product Engineer – Health Scores

Data becomes truly powerful when it’s distilled into simple, meaningful insights. At Ultrahuman, our Product Engineer – Health Scores will be the mastermind behind translating raw biomarker data into the elegant health metrics that guide our users. In this role, you’ll blend software engineering, data analysis, and a touch of physiology/biomedical science to create and refine the scores that users rely on (think metabolic score, recovery score, sleep quality index, etc.). Working in an engineering-driven culture that prizes first-principles thinking, you won’t just implement algorithms — you’ll question and reinvent them. Your work ensures that our users get actionable, science-backed insights from our devices, much like how a finely tuned engine yields peak performance.

Responsibilities:

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  Develop Health Scoring Algorithms: Design and implement the algorithms that calculate health scores from various data streams (HRV, heart rate, sleep patterns, glucose levels, etc.). Ensure these algorithms are rooted in scientific research and validated principles while also tailored to the unique data our Ultrahuman devices collect.
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  Iterate and Improve Metrics: Continuously refine the scoring models by incorporating new data and feedback. For instance, adjust the recovery score algorithm as we learn more about how users respond to specific training or sleep patterns. Use first-principles reasoning to adjust weightings or create new sub-metrics when needed, always asking, “What exactly are we trying to measure, and how can we measure it better?”
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  Cross-functional collaboration: Work alongside data scientists, physiologists, and product managers to align on what each health score should represent. Gather user feedback and clinical insights to ensure our scores truly help users make better health decisions. Collaborate with the app team to determine the best way to present these scores to users in an understandable fashion.
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  Efficient Implementation: Implement algorithms in a performant, scalable way (likely in our backend or possibly on-device for some metrics). Ensure computations can happen in near real-time when needed, and optimize code for low power usage if running on wearable firmware.
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  Validation & Testing: Establish validation protocols. This could involve comparing our health scores against external gold standards or outcomes (e.g., does our sleep score correlate with a polysomnography lab result? Does the metabolic score predict improvements in lab-measured biomarkers?). Work on generating data and analysis to prove out the accuracy and reliability of Ultrahuman’s health scores.

Requirements

• Educational Background: Bachelor’s or Master’s in Computer Science, Data Science, Biomedical Engineering, or related field. A strong foundation in algorithms and data structures is required; coursework or experience in signal processing or statistics is a plus.
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  Experience: 3+ years in a software or data-focused role. Experience building algorithms or models for real-world data (especially physiological or time-series data) is highly desirable. Could be from work in health tech, fintech risk scoring, gaming (scoring/user metrics), etc.
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  Programming Skills: Proficient in Python or another language for data analysis and prototyping. Able to implement final algorithms in production (Python, C++ or Java on backend, or even embedded C if needed on device). Familiarity with data analysis libraries (NumPy, Pandas) and possibly ML frameworks if using machine learning for scoring improvements.
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  Analytical Mindset: Comfortable with analyzing large datasets to derive insights. Experience with statistical analysis or validation techniques. You can think critically about what a number means and how it might mislead, ensuring our scores are both accurate and interpretable.
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  Domain Interest: A keen interest in human physiology, health, or fitness. You don’t have to be an MD or PhD, but you should be excited to learn about things like metabolism, sleep stages, cardiovascular health, etc., as they relate to the data you’re handling.

Preferred Experience:

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  Health Tech/Wellness Projects: Experience working on any kind of health metric or fitness score (maybe you’ve contributed to a sleep analysis feature, or a stress score in a wearable app) will give you a head start.
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  Machine Learning Knowledge: Familiarity with ML techniques for time-series or physiological data could be useful. For example, knowing how to apply filtering, anomaly detection, or predictive modeling to improve or personalize scores.
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  First-Principles Problem Solving: Examples from your past where you broke down a complex problem and solved it from basics. Perhaps you redesigned a scoring system or created a metric from scratch because the existing ones were inadequate.
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  Performance Tuning: Experience optimizing code for efficiency, especially if you’ve done any embedded or low-latency work. Since some algorithms might run on the ring or need to handle streaming data, this skill is valuable.