Founding Machine Learning Engineer, Health Algorithms

Fort is a San Francisco-based wearables company building a premium health tracking device for people who want to keep their bodies strong and capable for life. Our product helps people understand their training, recovery, and progress in a way that feels clear, useful, and motivating. We care about building something that feels refined, human, and deeply useful: part health companion, part beautiful consumer object, part new ritual for strength and longevity. We're a small founding team of former Tesla engineers building across hardware, software, health, and consumer brand.

Fort is building a premium health tracking device that helps people keep their bodies strong and capable for life. We combine wearable sensing, thoughtful product design, and intelligent software to turn training, recovery, and daily physiology into clear, actionable insight. Our first device is screenless, beautifully designed, and built around the idea that strength, recovery, and physical capability should be measured with the same seriousness as sleep, steps, or heart rate. Fort is hiring a Founding Health Algorithms Engineer to build the algorithms that turn raw wearable sensor data into reliable health and performance insights. This person will work across physiological signal processing, machine learning, sensor fusion, validation, and edge deployment. They will help Fort understand what can be measured, how confidently it can be measured, and when a metric should or should not be shown to a user. This is not a research-only role. We are looking for someone who can take messy real-world sensor data, develop robust algorithms, validate them properly, and help ship them into a real consumer product. What You'll Own You will own Fort's health and biosignal algorithm stack from data collection through production. You'll work with PPG, IMU, and other wearable signals to develop algorithms for health, recovery, activity, and training insights. You'll help define the data we collect, the quality standards we hold ourselves to, and the validation required before a metric becomes part of the product. A big part of this role is knowing how to debug ambiguity. When something fails, you should be able to reason through whether the issue is physiology, hardware, firmware, labeling, data quality, model behavior, or product interpretation. Responsibilities Define data collection requirements for Fort's health, recovery, activity, and training algorithms. Build PPG, IMU, and biosignal algorithms from raw data through production deployment. Develop signal quality metrics across the full sensing chain, from sensor behavior to user-facing output. Create systems for detecting, rejecting, and correcting motion artifacts in wearable sensor data. Build algorithms for heart rate, HRV, activity, recovery, fatigue, sleep-adjacent signals, exercise detection, and strength training analytics. Design validation studies that answer practical product questions, not just academic benchmark questions. Evaluate algorithm performance across different users, conditions, workouts, motion patterns, sensor placements, and populations. Define confidence scoring, failure modes, and "do not show metric" behavior for health insights. Work closely with firmware, mobile, hardware, and product teams to deploy algorithms on constrained devices and mobile environments. Optimize algorithms for low power, low latency, and efficient edge inference. Translate algorithm behavior into product decisions that are understandable, trustworthy, and useful to users.