Own end-to-end machine learning for edge-ready models, from sensor ingestion through deployment and operational MLOps processes.

Own the full ML lifecycle, starting from raw sensor data and ending with a model running on constrained hardware
Design and develop sensor data pipelines for continuous collection, cleaning, and organization
Build, train, optimize, and deploy signal processing and anomaly detection models for edge devices
Create and refine ML models for real-time device applications and ongoing performance improvements
Collaborate with embedded engineers to integrate and validate inference within device software
Work closely with firmware teams to embed and test AI features on hardware platforms
Build and maintain MLOps infrastructure to support regulatory and quality processes
Set up and manage tooling for experiment tracking, automated evaluations, and deployment management
Participate in sensor selection and validation
Document model development to support regulatory submissions and internal quality processes
Analyze model behavior, ensure reliability, and troubleshoot issues to maintain high-quality outputs

5+ years of experience in machine learning engineering or applied ML
Strong proficiency in Python
Hands-on experience with PyTorch or TensorFlow
Experience deploying models to the edge using TFLite, ONNX, CoreML, TensorRT, or an equivalent workflow
Experience building sensor data pipelines
Proficiency with MLOps
Solid software engineering fundamentals
Proficiency in C or C++
Degree in a relevant field

Experience with physiological signal processing for medical or wearable applications
Background in robotics or autonomous systems
Experience in a startup or small team

Senior Machine Learning Engineer

Job Description