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.