As an ML Engineer (SDE 2) at Ultrahuman, you will dive headfirst into our troves of health data, using your skills to make sense of it and build features that enhance our platform. This role is all about implementation and innovation: you’ll collaborate with the ML Lead to push the boundaries of what our devices and app can do with AI. Whether it’s fine-tuning an algorithm that boosts sensor accuracy or developing a chatbot that explains complex health metrics in plain language, your contributions will have a tangible impact on the lives of our users. In our engineering-first culture, you’ll be encouraged to approach problems from fundamentals and deliver solutions that are both elegant and effective.

Responsibilities:

• Enhance Biomarker Accuracy: Work on the front lines of improving our sensor data. Implement and experiment with algorithms to calibrate and enhance raw signals (heart rate, HRV, glucose, etc.) against reference standards. For example, you might program and test a machine learning model that compensates for motion artifacts in heart rate data, making our ring’s readings more reliable during exercise.

• Build Conversational Health Tools: Develop components of our conversational interface for user guidance. This could involve training NLP models or using existing frameworks to interpret user questions about their health data, and constructing responses that draw on our databases and health knowledge. You’ll work on things like intent recognition (“User asks about low energy levels”) and fetching relevant personalized insights (“Your recovery score was low due to poor sleep, which might explain your fatigue”).

• Intelligence for Support Teams: Create and refine internal tools that help our customer support. For instance, code a system that categorizes support tickets by topic using natural language processing or a model that predicts which users might need proactive outreach based on their data patterns. Ensure these tools integrate with our support workflows (e.g., feeding into a dashboard used by the support team).

• Data Analysis & Experimentation: Analyze large datasets to find patterns or issues. You might dig into sensor logs to find why a metric is occasionally off, or run experiments to compare different machine learning approaches for a problem. Document your findings and suggest next steps based on data.

• Collaboration & Learning: Work closely under the guidance of the ML Lead and collaborate with cross-functional teams (app engineers, hardware engineers, data scientists). You’ll take part in code reviews, design discussions, and brainstorming sessions. Continuously learn and adapt new tools or algorithms that could benefit Ultrahuman’s mission.

Requirements

• Educational Background: Bachelor’s in Computer Science, Engineering, Data Science or a related field (Master’s is a plus). Strong fundamentals in programming and understanding of basic machine learning concepts.

• Experience: 3-5 years in software engineering or ML engineering roles. Hands-on experience implementing machine learning models or data-driven features in production. You should be comfortable with the end-to-end process: data cleaning, model training, evaluation, and deployment.

• Technical Skills: Proficient in Python and ML libraries (such as scikit-learn, pandas, TensorFlow/PyTorch for deep learning). Familiarity with SQL and handling large datasets. Some exposure to NLP (NLU/NLG) techniques or libraries (like spaCy, Hugging Face transformers) is a plus, given the conversational UI aspect.

• Problem-Solving: Strong analytical thinking. Ability to troubleshoot issues in data or code – for instance, debug why a model’s performance dropped or why a certain pattern is appearing in sensor data.

• Software Engineering: Good coding practices (version control, writing clean and efficient code). Experience with cloud environments or MLops (Docker, AWS/GCP for model deployment, etc.) is beneficial.

Preferred Experience:

• Domain Relevance: Experience in any of the following areas: time-series data analysis, signal processing, health or fitness data, wearable devices, or even gaming/IoT data. This will help you ramp up faster in understanding Ultrahuman’s context.

• Cross-Disciplinary Projects: Past projects that show you can work with people outside of pure software – maybe you collaborated with designers to put an ML feature into a UI, or worked with an electrical engineer to improve firmware using data insights.

• First-Principles Mindset: Instances where you had to go beyond using a library or standard approach – such as writing a custom loss function for a unique need, or simplifying a complex model when data was limited. This inventive streak is highly valued.

• Continuous Improvement: Enthusiasm for learning. Perhaps you’ve participated in Kaggle competitions, taken advanced courses, or have personal projects in ML. Show us that spark of curiosity that goes beyond your day job.