Primary Skills: Computer Vision (Expert), Machine Learning (Advanced), PyTorch/TensorFlow (Proficient), Data Pipelines (Proficient), Python (Expert)
Contract Type: W2 and C2C
Location: Fremont, CA
Duration: 6+ months
Pay Range: $40 - $45 per hour. on W2 or C2C

Job Summary:
We are looking for a Machine Learning & Computer Vision Engineer to tackle advanced perception problems within our manufacturing environments, utilizing our unique internal datasets. The ideal candidate will develop, optimize, and deploy models for automated visual inspection and factory automation, contributing to the high-speed, precision manufacturing of clients vehicles. This role calls for strong collaboration with engineering teams, hands-on problem solving in factory settings, and continuous model improvement for real-time, robust application in production.

Key Responsibilities:

• Develop and optimize advanced vision and perception models for manufacturing quality inspection and defect detection.
• Work with multi-sensor input and robotics to refine object detection, localization, and attribute understanding in production environments.
• Train and adapt models on Clients-specific datasets to meet high accuracy and robustness standards.
• Create domain-specific solutions surpassing standard approaches, tailor-made for Clients automation and manufacturing requirements.
• Collaborate closely with engineering teams for the seamless integration of ML models into manufacturing systems and their subsequent performance monitoring.

Must-Have Skills:

• Proven expertise in Computer Vision, Machine Learning, and AI model development.
• Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow).
• Experience with large, domain-specific datasets; expertise in data labeling, preprocessing, and augmentation.

Domain Experience Required:
Experience in automation, robotics, or a closely related field is crucial for understanding the unique challenges and requirements of clients manufacturing environment.

ABOUT AKRAYA
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