Machine Learning / Data Mining Lead (SDE 3)

Ultrahuman is amassing one of the richest health datasets in the world – continuous streams of glucose readings, heart rates, sleep patterns, and more – all from users who trust us to help them improve their health. As the Machine Learning / Data Mining Lead (SDE 3), you will turn this ocean of data into intelligent features and improvements that set us apart. In the spirit of first-principles thinking, you and your team will delve into raw data to extract insights and build models that haven’t been seen in health tech before. This is a leadership role for a hands-on ML expert who can architect solutions and guide a team, much like how a seasoned engineer at Tesla might lead autopilot AI development. Your contributions will directly enhance our product’s accuracy, user experience (through smart insights and interfaces), and even internal operations.

Responsibilities:

• Benchmark & Improve Biomarker Accuracy: Lead efforts to benchmark Ultrahuman’s sensor data (heart rate, HRV, glucose, etc.) against gold-standard references. Develop calibration algorithms or machine learning models that improve the accuracy of our raw biomarkers, ensuring our wearables approach clinical-grade precision. For example, create models to correct any drift in the ring’s temperature sensor readings using environmental data or user-specific baselines.

• Intelligent Health Insights & Interfaces: Drive the creation of AI-powered features that make health data more accessible to users. This includes building conversational interfaces or virtual assistants within the Ultrahuman app – tools that let users ask questions about their data (“Why was my sleep score low last night?”) and get insightful answers. Utilize NLP and data mining to allow the system to interpret user queries and respond with personalized, easy-to-understand explanations or recommendations.

• Support Team Intelligence: Develop internal ML tools to empower our customer support and operations. For instance, build anomaly detection models that flag unusual user data patterns (which might indicate a device issue or a user health alert) so support can proactively reach out. Or create a recommendation system to help support reps answer user questions faster by suggesting likely solutions based on past interactions.

• Leadership & Strategy: Set the roadmap for ML and data science projects at Ultrahuman. Mentor and guide a small team of ML Engineers and Data Scientists (including the ML Engineer SDE2) – performing code and research review, providing technical direction, and ensuring high-quality delivery. Work closely with product managers and other engineering leads to prioritize ML projects that have the highest user and business impact.

• Deployment & Scaling: Oversee the full lifecycle of ML solutions from experimentation to production. Ensure models are not just accurate, but also robust in production (monitor performance, handle edge cases) and scalable to millions of users. Implement best practices for model training, versioning, A/B testing of new algorithms, and integrating models into our app or backend systems.

Requirements

• Educational Background: Master’s or PhD in Computer Science, Machine Learning, Data Science, or related field. (Or Bachelor’s with exceptional, extensive experience.) Strong theoretical foundation in machine learning, algorithms, and statistics.

• Experience: 7+ years in data science or ML engineering roles, with at least 2 years in a technical leadership position guiding other engineers or researchers. Proven track record of delivering AI/ML-driven features or products to market.

• Technical Mastery: Proficiency in Python and common ML libraries (TensorFlow/PyTorch, scikit-learn, pandas, etc.). Experience with data pipeline tools (Spark, Kafka, or similar) and deploying models in production environments (using cloud services or on-device inference). Solid understanding of both classical algorithms and deep learning techniques.

• Domain Knowledge: Experience with time-series data or biosignals is a strong plus (e.g., working with physiological data, wearable data, or other sensor streams). Familiarity with NLP for creating conversational agents or Q&A systems would be beneficial.

• Problem Solving & Creativity: Exceptional ability to break down complex problems and invent original solutions. You’ve likely tackled hard, open-ended problems and can demonstrate how your work made a significant improvement (e.g., improved accuracy by X%, reduced support tickets by Y through proactive modeling, etc.).

Preferred Experience:

• Health Tech or Related Industries: Direct experience in healthtech, medtech, or fitness analytics, working on things like biometric algorithms, predictive health models, or digital therapeutics. Alternatively, experience in high-stakes data domains (finance, autonomous vehicles, etc.) that require precision.

• Leadership & Communication: Experience presenting findings and proposals to executives or non-technical stakeholders. Ability to articulate the value of ML initiatives in terms of user impact or ROI.

• Full-Stack ML: Familiarity with the entire ML stack: from signal processing and feature engineering (especially for sensor data) to building customer-facing UI that surfaces ML results (e.g., worked with front-end teams to display your model’s output in an app).

• Continuous Learning: Evidence of staying at the forefront of the field (publications, participation in ML competitions, patents, or open-source contributions). A genuine passion for learning and applying new technologies.