Data Scientist / Machine Learning Engineer

Industry:

Semiconductor / Advanced Manufacturing

Location:

Hillsboro, OR

Work Arrangement:

100% Onsite

Employment Type:

Contract – W2

Rate:

$65/hr W2

Position Overview

We are seeking a Data Scientist / Machine Learning Engineer to develop and deploy advanced Machine Learning and Computer Vision solutions for semiconductor wafer inspection

The ideal candidate will have strong experience in machine learning, computer vision, deep learning, and production ML systems, with the ability to work across model development, evaluation, optimization, and deployment.

Key Responsibilities

Develop and deploy Machine Learning and Computer Vision models for semiconductor wafer inspection.
Build image-classification and defect-detection solutions using modern deep-learning techniques.
Develop models using PyTorch, CNNs, and Vision Transformers (ViTs)

.
Work with embeddings, similarity search, and metric learning for image analysis.
Apply few-shot, cold-start, and self-supervised learning techniques where labeled data is limited.
Develop approaches for uncertainty estimation, model calibration, and open-set detection

.
Optimize ML models for production inference and strict latency requirements

.
Develop and maintain production MLOps pipelines

.
Implement model monitoring, drift detection, regression testing, retraining, and staged deployment.
Analyze data quality and collaborate with domain experts to understand the underlying physics and limitations of inspection data.
Troubleshoot model and pipeline performance issues and improve system reliability.
Evaluate models using appropriate classification and reliability metrics.

Required Skills

5+ years of experience building and deploying Machine Learning / Computer Vision systems

, or a strong equivalent research background.
Strong programming skills in Python

.
Hands-on experience with PyTorch and deep-learning frameworks.
Strong understanding of Computer Vision and Deep Learning

.
Experience with image classification and/or image-based ML systems.
Understanding of:
CNNs
Vision Transformers
Embeddings
Metric learning
Few-shot learning
Self-supervised learning
Model calibration
Uncertainty estimation
Open-set detection
Strong knowledge of ML model evaluation and validation.
Experience with production ML / MLOps

.
Understanding of model monitoring, drift detection, retraining, and deployment.
Ability to optimize models for production inference and latency.

Preferred Qualifications

Experience in semiconductor manufacturing or inspection

.
Experience with wafer inspection, defect detection, microscopy, or industrial imaging.
Experience working with highly imbalanced datasets.
Experience with anomaly detection or previously unseen-class detection.
Experience with model optimization and production inference.
Experience working with domain experts and engineering teams.
Experience with cloud, Docker, Kubernetes, or related deployment technologies.

Ideal Candidate Profile

The ideal candidate combines strong ML/CV engineering skills with the ability to take models from experimentation into production.

Candidates should be comfortable discussing the complete ML lifecycle:

Data → Preprocessing → Model → Evaluation → Calibration → Deployment → Monitoring → Retraining

Strong communication and problem-solving skills are important because the role involves close collaboration with engineering and domain experts.

Interview Process

Interview Format:

Onsite
Location:

Hillsboro, OR
Interview:

Final/onsite interview
Candidates should be prepared to discuss previous ML/CV projects, technical decisions, model evaluation, production deployment, and troubleshooting.

Job description