Overview

About Meeami Technologies:

Responsibilities

• 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.
• 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:
• arrhythmia detection
• 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.
• Define data strategies for:
• 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.
• Proficiency in Python and the scientific stack (NumPy, SciPy, pandas, scikit-learn). MATLAB is a plus.
• Solid understanding of the physiology behind the signals you work with.
• 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.