We are seeking a hands -on Machine Learning Engineer to join our research and
engineering team building AI -driven navigation and event -detection systems for
GNSS -degraded environments.
You will work closely with our Research Scientist to design, train, and deploy ML models
that interpret multi -sensor data from smart devices for real -world applications in navigation.
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.
Requirements
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).
- Robust 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.