Machine Learning Engineer

InnerGize

Machine Learning Engineer

This position focuses on developing and refining CNN-based models from data collection through to deployment.

Job Requirements

• Design, train, and optimize CNN architectures for correct wearable patch placement detection using available sensor data.
• Build and maintain data pipelines for collection, labeling, augmentation, and validation of training datasets.
• Evaluate model performance and iterate to improve accuracy and robustness across users.
• Prepare models for deployment on mobile or embedded systems (optimizing for latency and size).
• Document experiments, models, and performance metrics clearly for internal teams.
• Collaborate with data, product and firmware teams to integrate model outputs into the InnerGize ecosystem.

Education Requirements:

• B.Tech / B.E. in Computer Science or a related field with specialization in Machine Learning, OR

• Master’s / Ph.D. in Machine Learning, Data Science, or Applied Mathematics (preferred).

• Experience:

• 2–4 years of professional experience developing machine learning models, with a strong focus on CNN architectures.

• Technical Skills:

• Proficiency in Python and frameworks such as PyTorch or TensorFlow.

• Demonstrated experience designing, training, and deploying CNNs for classification or regression tasks.
• Ability to work with real-world datasets, including preprocessing and augmentation.
• Familiarity with on-device or edge deployment tools such as TensorFlow Lite or ONNX (a plus).

• Soft Skills:

• Strong analytical and problem-solving skills.

• Good communication and documentation abilities.
• Comfortable working cross-functionally with other teams.
  

Preferable Skills:

• Exposure to time-series or sensor-based data.
• Understanding of mathematical/statistical models for signal behavior.
• Prior experience deploying models on constrained devices.
  

₹12–18 LPA