Deep Learning & Machine Learning Engineer (Biomedical Signals)

Location: Bengaluru, India

Job Type: Full-time

Job Description:

We are seeking a talented Deep Learning & Machine Learning Engineer specializing in biomedical signals to join our dynamic team. The ideal candidate will develop advanced algorithms and models to process and analyze complex biomedical data, such as ECG, EEG, EMG, and other physiological signals, using Python and state-of-the-art machine learning and deep learning frameworks.

Key Responsibilities:

Develop Machine Learning & Deep Learning Models: Design, implement, and optimize models for analyzing biomedical signals using Python, TensorFlow, PyTorch, Scikit-learn, and other relevant tools.
Signal Preprocessing: Implement signal preprocessing techniques (e.g., noise filtering, feature extraction, signal normalization) for ECG, EEG, EMG, and other physiological data.

Feature Engineering: Extract relevant features from biomedical signals to improve model performance and interpretability.

Model Training and Validation: Perform model training, hyperparameter tuning, and cross-validation to ensure model robustness.

Integration with Clinical Applications: Collaborate with clinicians and researchers to integrate predictive models into diagnostic or monitoring systems.

Research & Innovation: Stay up to date with the latest research in the field of biomedical signal processing and machine learning techniques.

Data Visualization: Design effective visualizations for signal data and model outputs for better interpretability and decision-making.

Documentation: Maintain clear documentation of model architectures, experiments, and results.

Required Qualifications:
Education: Bachelor’s or Master’s degree in Computer Science, Biomedical Engineering, Data Science, Electrical Engineering, or a related field. Ph.D. is a plus.

Experience:
2+ years of experience in machine learning or deep learning, especially in the field of biomedical signal processing.
Hands-on experience with signal processing techniques and libraries such as NumPy, SciPy, MNE-Python, etc.
Experience with frameworks like TensorFlow, PyTorch, or Keras.

Technical Skills:
Proficiency in Python and relevant libraries (e.g., NumPy, Pandas, Scikit-learn, Matplotlib).
Experience in working with biomedical signals such as ECG, EEG, EMG, etc.
Expertise in neural network architectures, including CNNs, RNNs, LSTMs, and their application to time-series data.
Knowledge of classical machine learning algorithms (e.g., SVMs, Random Forests) for biomedical applications.
Familiarity with cloud-based platforms for large-scale data processing and model deployment is a plus (e.g., AWS, GCP).
Analytical Mindset: Strong problem-solving skills and ability to develop solutions for complex signal processing tasks.
Communication: Ability to communicate technical concepts to both technical and non-technical audiences.

Preferred Skills:
Experience in Healthcare/Biomedical Sector: Prior experience in healthcare, medical devices, or clinical data analysis.
Publications or Research: Published papers in reputable journals or conferences on topics related to machine learning in biomedical signal processing.
Visualization and Interpretation: Experience with data visualization tools like Plotly, Seaborn, or other dashboard tools.
Experience with Regulatory Standards: Familiarity with medical device regulations (e.g., FDA, CE) for AI/ML applications is a plus.