Founded in Tel Aviv in 2016, UVeye is a computer vision and data company that creates automatic vehicle inspection systems to encourage road safety and increase transparency across the automotive industry. With key partnerships including General Motors, Volvo, and CarMax among others, our technology is utilized in manufacturing plants, dealerships, wholesale auctions, delivery fleets, security checkpoints and more.

Want to be an important part of setting the standard for safety in the auto industry? Check out the description below to see if you’re a great fit!

We are looking for a Machine Learning Software Engineer to join us. In this role, you will develop efficient and scalable code to create and operate Machine Learning systems for our production and Infra environments.

Here’s what you’ll be doing:



• Productization of algorithms - Optimized for high scale algorithm serving.
• Code ML Infrastructure & Framework for Agile ML Lifecycle, MLOps, and CI/CD.
• Continuously explore ways to improve our code and ML Infrastructure.
• Collaborate with team members to define future technology and features.
  
  

Requirements:

• B.Sc. in Computer Science or industry equivalent.
• Over 7 years of Backend engineering development.
• Strong working experience in Python development.
• Working experience with building or maintaining cloud-native applications.
• Working experience with Linux/Unix.
• Working experience with VEnv, Docker, K8S.
• Experience with optimizing algorithm implementations.
  
  

Bonus if you have:

• Experience with Major Deep Learning Frameworks (such as PyTorch, TensorFlow, etc.). Comfortable in unexplored territory.
• Experience with ML development and MLOps.
• Experience with optimizing algorithm implementations.
• Experience with GPU/CPU optimizations.
• Experience with Real-time/Embedded development.
  
  

Check out our Life at UVeye page to learn more about the employee experience.

UVeye is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status