Senior Machine Learning Engineer

Job Description

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.