Deep Learning Research Engineer - ML & Physics - Q
Core
Design and implement novel neural network architectures for complex physical sensing, reconstruction, and inverse problems using data-driven learning and deep physical understanding.
Role type
Senior IC deep learning research engineer (ML & Physics)
Builds
High-performance deep learning models for sensing platforms
Domain
Consumer electronics, optical measurement, physics-informed machine learning
Deliverable
production ML models
Required skills
PyTorch, Python, neural network architecture design, custom loss functions, training pipeline development, hyperparameter optimization, failure-mode analysis, cross-functional collaboration
Preferred skills
Physics-informed neural networks (PINNs), neural operators (FNO/DeepONet), distributed training, GPU acceleration, top-tier conference publications
Technologies
PyTorch, Python
Responsibilities
Conceive and implement novel neural network architectures for physical sensing; lead end-to-end research from data preparation to validation; build and validate deep learning models; formulate custom loss functions and regularization; design benchmarking suites; prototype proof-of-concepts; collaborate with physicists and optical engineers
Seniority
Senior, hands-on IC
