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:
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You will develop new algorithms that consume raw sensor data input to detect defect segmentation and location information on high-resolution images or 3D inputs, with pixel-level precision.
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You will engineer software that runs on the production line 24/7 to efficiently execute our algorithms in real time with each decision made under 20ms.
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You will develop metrics and tools to evaluate various model performance and improve visibility and interpretation of our system.
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You will explore new solutions and push the boundaries of the field including using Stable Diffusion, SAM and build applications for critical missions in the manufacture fields.
Who You Are:
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Bachelor’s degree in Computer Science, Mathematics, Physics, or a relevant technical field, or equivalent practical experience. Solid foundational and applied math knowledge.
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2+ years of experience in building machine learning models for computer vision in production.
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Strong theoretical background and practical experience in Deep Learning, with proficiency in PyTorch or Tensorflow. Excellent Python skills for writing efficient and maintainable solutions in large code bases.
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Strong communication and decision-making skills. Ability to explain the rationale behind experiments and make decisions to balance exploration and exploitation.
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Resilience in uncertain and complex environments.
Preferred Qualifications:
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Master’s degree in Computer Science, Mathematics, Physics, or a relevant technical field.