AI ML Engineer

We're building something meaningful — not just another dashboard or data toy. This role is for someone who enjoys working with **real human behavior data** , where every model you ship has the power to improve how people move, live, and take care of themselves. If you love solving puzzles inside messy, real\-world datasets, you'll feel at home here. **What You'll Work On** * Build, train, and refine machine learning and deep learning models using time\-series, sensor, and behavioral data. * Integrate data from wearables, fitness tracking platforms, and device APIs to create a clear story from movement, patterns, and activity signals. * Develop and maintain data pipelines that support both batch and real\-time analytics. * Own model deployment in production environments — your models won't live in notebooks; they'll live in the world. * Work closely with engineering teams to integrate ML models into mobile and web apps. * Support logic for fraud, spoofing, and anomaly detection, ensuring data reflects real human activity. * Make complex outputs easy to understand — not just for engineers, but for product and business users too. **Requirements** ****You'll Thrive Here If You Have**** * 5\+ years of hands\-on experience as an ML Engineer or Applied Scientist. * Strong foundation in machine learning, deep learning, and time\-series analysis. * Experience working with wearables, IoT data, or sensor\-based datasets. * Fluency in Python, PyTorch or TensorFlow, and good software engineering habits. * Experience building and shipping production ML systems using modern MLOps practices. * Comfort with Node.js, APIs, and backend integration workflows. * Understanding of data privacy, cloud ML infrastructure (AWS, GCP, or Azure), and edge inference. * A solid grasp of feature engineering, statistical reasoning, and evaluating what "good" looks like in a model. ****The Kind of Person We're Looking For**** * You enjoy going deep and figuring things out. * You care about clarity — in your code, in your thinking, in how you explain your work. * You see data not just as numbers but as stories about real people. * You value responsibility. When something is yours, you own it end\-to\-end