Machine Learning Engineer

Pittsburgh, PA | Full-Time | On-Site

Glintect is a high-velocity startup engineering custom machine vision systems for automated visual inspection across manufacturing industries. Our platforms enable production facilities to detect and flag defects in real time — before non-conforming product ever leaves the line.
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We are seeking a talented Machine Learning Engineer to design, train, and deploy defect detection models across our suite of industrial inspection products. You will work directly with production image data, build model pipelines from annotation through deployment, and iterate rapidly based on real-world performance metrics.
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You’ll collaborate closely with our computer vision and software engineers to integrate models into live production systems. The emphasis is on accuracy, inference speed, and reliability in demanding manufacturing environments.

What You'll Do
• Design, train, and evaluate deep learning models for visual defect detection on industrial imagery (classification, segmentation, anomaly detection).
• Build and maintain end-to-end ML pipelines: data ingestion, annotation management, model training, validation, and deployment.
• Work with annotators and domain experts to develop high-quality labeled datasets tailored to specific manufacturing defect types.
• Optimize models for real-time inference on edge hardware, balancing accuracy against latency and compute constraints.
• Monitor deployed models in production, diagnose performance degradation, and implement continuous improvement cycles.
• Collaborate with computer vision engineers to integrate models into camera-based inspection pipelines.
• Document experiments, track model versioning, and maintain reproducible training workflows.

What We’re Looking For
• 3+ years of hands-on experience training and deploying deep learning models, preferably in computer vision applications.
• Strong proficiency in Python and deep learning frameworks (PyTorch preferred).
• Experience with object detection and segmentation architectures (YOLO, Mask R-CNN, or equivalent).
• Familiarity with edge deployment toolchains (ONNX, TensorRT, OpenVINO) is a strong plus.
• Solid understanding of data augmentation strategies and class imbalance handling for industrial inspection datasets.
• Experience managing datasets and experiments using tools such as MLflow, Weights & Biases, or equivalent.
• Strong analytical mindset with a bias toward empirical validation over theoretical assumptions.
• Comfortable operating in a small, fast-moving team where ownership and initiative are expected.

Compensation: This role offers a competitive base salary, comprehensive healthcare benefits, and eligibility for early employee equity participation.
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Location: The preferred candidate will be based in Pittsburgh, PA and willing to work in person at the company’s Uptown office.
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To Apply: Send your resume, a brief introductory note, and any supporting materials to zach@glintect.com.

Date Posted: March 28, 2026