DUST Identity has developed a supply-chain security solution that uses microscopic diamonds to securely tag physical objects so they are uniquely identifiable. Think of a barcode, but invisible, unfalsifiable, and with diamonds! We aim high and need self-starters and problem solvers who thrive when challenges are great, and potential is greater. Learn more at dustidentity.com.

The Job

We are looking for a Machine Vision Engineer to join our growing R&D team. We are looking for knowledge and experience, but above all care about character and cultural fit. The Machine Vision Engineer will drive improvements in DUST's core technologies across a wide range of use cases and deployment scenarios.

The Machine Vision Engineer role requires a hands-on, results-oriented individual who is organized, a great communicator, and able to work in a fast-paced environment. The successful candidate will possess a technical background that enables them to identify, test, and deploy cutting-edge algorithms and data-processing techniques. Additionally, the candidate should have strong communication skills in order to explain their chosen technical approach and tradeoffs encountered when tackling problems. They should also have a demonstrated ability to execute within continuous development cycles.

What You'll Do

  • Develop machine vision algorithms and solutions
  • Develop performance testing and validation methodologies and reliable performance measures for algorithm evaluation
  • Drive the development of new tooling in support of the above
  • Design and implement machine learning models
  • Effectively communicate needs, challenges, and trade offs, with the rest of the engineering team

What You Bring

  • Deep knowledge of computer/machine vision fundamentals
  • MS in Computer Science, Engineering, or similar fields
  • 5+ years successful track record of working on machine vision products
  • Experience writing technical reports, Standard Operating Procedures, and documentation
  • Experience with traditional machine learning, as well as deep learning, specifically models used for image classification, filtering, segmentation
  • Feature extraction from images using established and cutting-edge deep learning approaches
  • Semantic Image Segmentation
  • Efficient and scalable image indexing and retrieval for large datasets
  • Python, OpenCV, numpy/scipy/etc., scikit-learn/image, jupyter
  • TensorFlow or PyTorch
  • Excellent mathematical reasoning skills; strong foundations in probability and statistics

Extra Credit

  • Experience with hardware optimization and/or image sensor integration (e.g., DSPs, ASICs, FPGAs)
  • Unsupervised, learning-based feature extraction techniques
  • Sensor error modeling and propagation
  • Ph.D. in Computer Science, Electrical Engineering or a related field

Start Date: Immediately; Must have legal right to work in the United States without sponsorship.
Location: Princeton, NJ / Remote
Compensation: Market Competitive + Stock option plan