Physiological Signal Processing Engineer (rPPG / Mobile Biosensing)

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We are hiring a Physiological Signal Processing Engineer to build and own production-grade physiological signal pipelines derived from smartphone camera video. This role focuses on extracting robust, clinically meaningful digital biomarkers under real-world conditions such as motion, lighting variability, occlusion, and device heterogeneity. This is a core signal engineering role, not a data science or model-training position.

Key Responsibilities

• Design, optimize, and maintain rPPG / TOI signal-processing pipelines using RGB and IR video
• Implement multi-stage preprocessing and denoising techniques to suppress motion and illumination artifacts
• Develop and apply signal quality scoring and confidence metrics for physiological signals
• Apply advanced signal-processing methods including: Adaptive filtering, CA / PCA-based approaches, Color-space transformations, Skin ROI detection and stabilization
• Build cross-device normalization strategies and error-bounded estimators
• Define biomarker-level acceptance criteria, failure modes, and signal validity thresholds
• Validate algorithms using ground-truth physiological references
• Collaborate with iOS and ML teams to integrate algorithms into on-device or hybrid pipelines
• Produce technical documentation suitable for regulatory review and platform approvals (including Apple review support)

Expected Deliverables

• Comprehensive benchmark reports across device models, skin tones, lighting conditions, and motion scenarios.
• Biomarker feature specification sheets, including recommended thresholds and confidence bands.
• A/B evaluation results demonstrating improvements in signal stability, missingness, and downstream inference performance.

Required Qualifications

• 5+ years of experience in physiological signal processing or computational biomedical engineering
• Strong hands-on experience with:
• rPPG and camera-based physiological sensing
• HR / HRV estimation and artifact mitigation
• Time-series signal analysis under real-world conditions
• Strong programming skills in Python
• Experience with at least one real-time or production runtime:
• C++, Swift, Metal, or equivalent
• Proven experience validating algorithms with ground-truth comparisons (not lab-only or synthetic data)

Nice to Have

• Experience with camera-based BP-adjacent proxies
• Exposure to stress or autonomic nervous system (ANS) metrics
• Experience with multimodal signal fusion
• Publications or commercial work in consumer-grade biosensing
• Experience building or supporting wellness-grade claims frameworks

Important Note

• This role is

  not

  a general ML or data science position
• Candidates focused primarily on model training without strong signal-processing fundamentals may not be a good fit
• Deep understanding of signal behavior, noise sources, and algorithmic trade-offs is essential