Unitxlabs
Machine Learning Engineer
Milpitas, CA - HQ. Posted 4 months ago
The role
You will design, train, and optimize deep learning models for defect detection and segmentation. You will build automated pipelines and deploy scalable ML pipelines on edge devices and cloud-based systems.
What you will do
- Design, train, and optimize deep learning models for defect detection and segmentation
- Develop and maintain automated pipelines for data preprocessing, augmentation, and model training
- Develop scalable ML pipelines for deployment on edge devices and cloud-based systems
- Optimize model inference for real-time decision-making
- Work with software engineers, robotics specialists, and hardware teams to integrate ML solutions
- Implement automated monitoring and alerting systems to track model drift and failures
- Create frameworks to assess model accuracy, precision, recall, and efficiency
- Design visualization dashboards, conduct A/B testing, and leverage explainable AI
- Investigate emerging AI/CV techniques
- Develop novel architectures and contribute to academic publications, patents, and open-source projects
Required qualifications
- Bachelor's degree (or foreign equivalent) in Computer Science, Mathematics, Statistics, or a closely related discipline
- 3+ years of experience in the job offered or a related occupation
- 3 years of hands-on experience in Deep Learning & Neural Networks
- 3 years of hands-on experience in Machine Learning Pipeline Development
- 3 years of hands-on experience in Sensor Fusion & Signal Processing
- 3 years of hands-on experience in Computer Vision & Real-Time Inference Optimization
- 3 years of hands-on experience in Statistical Modeling & A/B Testing
Benefits
- Full Medical
- Dental
- Vision
- 401k
- Unlimited PTO
- Daily meals provided
Before you apply
- Send resume and cover letter to james@unitxlabs.com
- Include Ref#MM0426 in the subject line
About the company
UnitX builds physical AI systems to automate repetitive visual tasks in factories. It is a fast-moving startup with a team from Stanford, MIT, Google, and beyond.