Computer Vision and Deep Learning (Mandatory) with Manufacturing/Semi-Conductor Manufacturing Domain Experience.
Job Information:
Position: Data Scientist
Location: Santa Clara, CA
Duration: Full Time
Job Description:
Role Mission:
we are evolving our AI capabilities from traditional image processing to cutting-edge Deep Learning architectures. As a Lead Data Scientist, you will be the technical authority for our Computer Vision department. You will transition our existing "Imaging" workflows into high-performance, scalable Deep Learning models that solve high-impact problems for our clients.
Qualifications & Experience:
· Education: BS or MS in Computer Science, Electrical Engineering, Mathematics, or a related field (or equivalent deep industry experience).
· Experience: 6-10 years in Data Science, with at least 3+ years specifically focused on Deep Learning and Computer Vision in a production environment.
· Leadership: Proven experience leading a project from ideation to a revenue-generating or cost-saving production state.
· Communication: Ability to explain complex $O(n)$ complexity or backpropagation logic to non-technical executive stakeholders.
Required Technical Expertise
1. Deep Learning & Neural Architectures
· Deep expertise in State-of-the-Art (SOTA) models such as YOLOv8/10, Detectron2, Vision Transformers (ViT), and MAE (Masked Autoencoders).
· Solid understanding of the mathematical foundations:
o Optimization algorithms: Adam, RMSProp, SGD.
o Loss function customization: Focal Loss, Dice Loss, Triplet Loss.
· Experience with Transfer Learning and Fine-tuning Large Vision Models (LVMs).
2. Core Computer Vision (The Foundation)
· While we are moving toward DL, you must still be proficient in OpenCV for preprocessing: Filtering, Binary Morphology, Perspective/Affine Transformations, and Edge Detection.
· Experience in Feature Extraction and Image Segmentation (Semantic & Instance).
3. Software Engineering & MLOps
· Production Python: Expert level; ability to write clean, modular, and testable code.
· Frameworks: Mastery of PyTorch (preferred) or TensorFlow/Keras.
· Deployment: Experience with Docker, Kubernetes, and model serving (FastAPI, NVIDIA Triton, or AWS SageMaker).
· Version Control: Advanced Git workflows and CI/CD for Machine Learning (DVC, MLflow).
