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Machine Learning Engineer @ UnitX

Unitxlabs

San Francisco, CAOn-siteFull-time$140k – $220k / year

About this role

Machine Learning Engineer @ UnitX - Back to UnitX’s Job Listings Machine Learning Engineer Location HQ Employment Type Full time Location Type On-site Department Software Compensation - $170K – $200K • Offers Equity Job Title: Machine Learning Engineer About Us: UnitX builds the world's leading physical AI systems to automate repetitive visual tasks in factories. UnitX is a fast-moving startup with a team from Stanford, MIT, Google, and beyond. Since inception, UnitX has deployed 1,000+ mission-critical systems across 190+ of the world's leading manufacturers' production lines. Every year, $15B worth of products go through UnitX's AI inspection system to ensure quality. Join us for the rare opportunity to work on computer-vision-driven products that go beyond the state of the art and that improve global manufacturing efficiency. What You'll Do: Develop & Optimize Machine Learning Models: Design, train, and optimize deep learning models for defect detection and segmentation using high-resolution images and 3D sensor inputs. You will implement advanced computer vision techniques (including Stable Diffusion, Segment Anything Model (SAM), and transformer-based architectures) to ensure pixel-level precision and real-time inference. Build Automated Pipelines: Develop and maintain automated pipelines for data preprocessing, augmentation, and model training to continuously optimize deployed solutions. Deploy & Maintain Production-Ready Software: Develop scalable ML pipelines for deployment on both edge devices and cloud-based systems. You will optimize model inference for real-time decision-making, ensuring processing times remain under 20 milliseconds. Collaborate Cross-Functionally: Work closely with software engineers, robotics specialists, and hardware teams to seamlessly integrate ML solutions into fully automated industrial workflows. Monitor & Ensure Reliability: Implement automated monitoring and alerting systems to track mode

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