Biomedical AI Engineer – Physiological Signal Processing

Meeami Technologies, Inc.; Hyderabad; On-site; INR 1,800,000 - 2,600,000; Full time

Benefits offered by this job

Restaurant allowance

Job summary

Meeami Technologies, a pioneer in on-device AI for biosignals, seeks a Biomedical AI Engineer to transform raw sensor data (ECG, PPG, EMG, EEG) into deployable models for embedded and cloud targets. You will own end-to-end signal processing, feature extraction, model development, and validation in collaboration with cross-functional teams.

Ideal candidates hold an MS or PhD with hands-on experience on physiological sensors, strong DSP, and ML/DL expertise (PyTorch/TensorFlow).

Qualifications

• MS or PhD in Biomedical Engineering, Electrical Engineering (signal processing), or a related field.
• Hands-on experience developing algorithms with physiological sensors such as ECG, PPG, EMG, or EEG.
• MS: 3+ years; PhD: 1+ year.
• Strong DSP foundation, including filtering, spectral and time-frequency analysis, and adaptive filtering.
• Experience applying ML/DL to time-series data (CNNs, RNNs/LSTMs, Transformers) using PyTorch or TensorFlow.
• Proficiency in Python and the scientific stack (NumPy, SciPy, pandas, scikit-learn). MATLAB is a plus.
• Experience with real biomedical datasets, public datasets like PhysioNet / MIT-BIH.
• Solid understanding of the physiology behind the signals.

Responsibilities

• Design and implement signal processing pipelines for physiological signals.
• Develop ML/DL models for health applications such as heart rate, HRV, SpO2, respiration, muscle activity.
• Combine DSP with deep learning and benchmark against reference devices.
• Optimize models for low-power hardware via quantization, pruning, and fixed-point implementation.
• Build scalable cloud pipelines for ingesting and processing wearable and clinical data.
• Collaborate with product teams and clinical advisors to translate requirements into algorithms.
• Document algorithms and validation results for patents, publications, and regulatory submissions.
• Stay current with biosignal processing and edge AI research.

Skills

DSP fundamentals

Python

ML/DL for time-series

PyTorch

TensorFlow

Time-series ML

MATLAB

PhysioNet / MIT-BIH familiarity

C/C++

Signal processing

Education

MS in Biomedical Engineering

MS in Electrical Engineering (signal processing)

PhD in Biomedical Engineering / related field

Tools

MATLAB

Job description

Meeami Technologies builds on-device AI for speech, audio, and multimodal intelligence, powering natural human-machine interaction on low-power hardware. We are now applying that edge-AI expertise to healthcare: turning physiological signals from wearables and medical devices into continuous health monitoring, early detection, and decision support.

At Meeami, you'll work where biomedical science, signal processing, and machine learning meet, building algorithms that run reliably in the real world.

Role Overview:

We're looking for a Biomedical AI Engineer to turn raw sensor data (ECG, PPG, EMG, EEG, IMU) into robust algorithms and ML models. You'll own the full path from signal to deployed model: acquisition, preprocessing, feature extraction, model development, validation, and optimization for embedded and cloud targets.

This role suits biomedical engineers with an MS or PhD who have built algorithms on physiological sensor data and want to see that work ship in products.

Key Responsibilities:

• Design and implement signal processing pipelines for physiological signals. This includes filtering, artifact and motion-noise removal, peak and beat detection, segmentation, and signal quality assessment.
• Develop algorithms and ML/DL models for health applications such as:
• heart rate and HRV estimation
• SpO2 and respiration estimation
• muscle activity analysis
• sleep and stress monitoring
• Combine classical DSP with deep learning where each fits best, and benchmark against established reference methods and devices.
• collection protocols
• annotation guidelines
• labelling with clinical experts
• dataset curation and versioning
• Build evaluation frameworks with clinically meaningful metrics, such as sensitivity, specificity, PPV, and error against reference devices. Test across subjects, devices, and real-world conditions.
• Work with embedded engineers to optimize models for low-power hardware through quantization, pruning, and fixed-point implementation, while meeting latency and memory budgets.
• Build scalable cloud pipelines for ingesting and processing wearable and clinical data.
• Collaborate with product teams, clinical advisors, and cross-functional teams across Meeami to translate requirements into working algorithms.
• Document algorithms and validation results in a way that supports patents, publications, and future regulatory submissions.
• Stay current with research in biosignal processing, wearable health, and edge AI.

Qualifications:

• MS or PhD in Biomedical Engineering, Electrical Engineering (signal processing), or a related field.
• Hands-on experience developing algorithms with physiological sensors such as ECG, PPG, EMG, or EEG, from industry or research:
• MS: 3+ years
• PhD: 1+ year
• Strong DSP foundation, including filtering, spectral and time-frequency analysis, and adaptive filtering.
• Experience applying ML/DL to time-series data (CNNs, RNNs/LSTMs, Transformers) using PyTorch or TensorFlow.
• Proficiency in Python and the scientific stack (NumPy, SciPy, pandas, scikit-learn). MATLAB is a plus.
• Experience with real biomedical datasets, clinical or wearable, including noisy and motion-corrupted data. Familiarity with public datasets such as PhysioNet or MIT-BIH.
• Solid understanding of the physiology behind the signals you work with.

Preferred Experience:

• Algorithms deployed on wearables or embedded platforms such as ARM Cortex-M, DSPs, or TinyML frameworks (TFLite Micro, ONNX Runtime). Working knowledge of C/C++.
• Prior work on healthcare products or at a medtech startup.
• Familiarity with medical device standards and regulation, such as IEC 62304, ISO 13485, FDA SaMD/510(k), CE MDR, or CDSCO.
• Exposure to multimodal data, such as biosignals combined with accelerometer data, heart and lung sounds, or voice biomarkers.
• Publications or patents in biomedical signal processing or health AI.
• Familiarity with sensor hardware and analog front-ends.

Why Join Meeami:

You'll help build Meeami's biomedical AI capability from the ground up, working closely with leadership on the technical roadmap. Your algorithms will run on real devices, not just in papers. Meeami's existing strength in edge AI and audio opens unusual opportunities in multimodal health sensing. This is a rare chance to shape both the science and the systems behind next-generation health monitoring.