Principal Machine Learning Engineer
Core
Lead the deployment of physics AI models into industrial manufacturing and maintenance (MRO) environments, bridging simulation outputs with shop-floor sensor data to drive hardware innovation.
Role type
Principal Machine Learning Engineer (Delivery)
Builds
Production ML systems for industrial process and sensor data, including pipelines, APIs, and model fusion for aerospace, automotive, and energy sectors.
Domain
Industrial manufacturing, MRO, aerospace, automotive, semiconductors, materials, energy & renewables
Deliverable
production ML models
Required skills
End-to-end ML system design, sparse and drifting sensor data handling, physics-informed modeling, uncertainty quantification (UQ), model transferability, Python, PyTorch, FastAPI, Kubeflow
Preferred skills
Experience in aerospace or regulated manufacturing, 3D point-cloud/mesh data processing
Technologies
Python, PyTorch, Pandas, FastAPI, SciPy, Kubeflow
Responsibilities
Own ML approach from modeling to deployment in customer production environments (cloud, on-prem, edge); Build production ML on sparse, drifting, miscalibrated process and sensor data; Choose between physics simulation, reduced-order and learned models; Set UQ and validation standards; Make models transfer across machines and material lots; Define product requirements for manufacturing and MRO; Lead scoping and architecture for data and ML systems; Mentor engineers and data scientists.
Seniority
Principal, technical leadership and mentorship
