Machine Learning Engineer

Key Responsibilities:

• Architect and implement self-supervised learning frameworks (masked prediction,contrastive learning, temporal order prediction, etc.).
• Design and train transformer encoders for high-dimensional time-series data.
• Build data pipelines and augmentation strategies for large, noisy sequential datasets.
• Fine-tune shared backbones for multi-task problems (classification + regression).
• Optimize and export models for edge / mobile inference (TFLite, Core ML, ONNX, quantization).
• Develop and train time-series models (LSTM, GRU, 1D CNN, Transformer) to detect user motion and events.
• Design data preprocessing pipelines including synchronization, normalization, segmentation, and feature extraction.
• Collaborate with Researcher to integrate physics-based features into ML model training.
• Perform hyperparameter tuning, validation, and cross-device generalization testing across smart devices.
• Build tools for dataset management, labeling, and feature visualization.
• Maintain documentation, experiment tracking, and reproducibility logs.

Qualifications & Skills:

• B.Tech/M.Tech or Ph.D. in Computer Science, AI/ML related field.
• 0-3 years of experience developing ML solutions for time-series or sensor data.
• Hands-on experience with self-supervised or contrastive learning (e.g., SimCLR, MoCo, MAE, BYOL, BERT-style pretraining).
• Strong grounding in time-series / sequential modeling (Transformers, RNNs, CNN-1D).
• Solid understanding of data preprocessing and normalization for continuous sensor signals.
• Strong proficiency in Python, PyTorch/TensorFlow, NumPy, Pandas, and Scikit-learn.
• Hands-on experience with sequence models (RNN/LSTM/GRU/CNN).
• Familiarity with MLOps tools (Weights & Biases, MLflow) and edge deployment pipelines.
• Strong debugging, version control (Git), and collaborative documentation habits.