Machine Learning Engineer

The R&D team (located in Los Angeles, CA) is involved with research and development of innovative solutions to medical imaging applications,  including disease detection/quantification in medical scans, disease risk stratification, image synthesis, text report mining,  and more! We are currently hiring both full-time and interns to join our R&D team.

Responsibilities:

• Develop deep learning models for prototyping and production purposes according to product feature request
• Design, implement and test model experiments using major deep learning frameworks
• Document experiments findings and results with supporting summary statistics for peer discussion and review (Confluence)
• Provide insights to data collection and annotation and collaborate with the data team for in-house data management and labelling
• Write production and deployment code (dockerization), iterate deployed models for optimal performance and inference speed
• Conduct methodology research in deep learning to drive scalable, real-time implementation

Qualifications

Basic Qualifications

• MS degree in computer science, engineering, or mathematics
• 2-3 years of relevant experience in building deep learning solutions for computer vision problems
• Proficient with at least one major deep learning framework, preferably TensorFlow/Pytorch
• Proficient in Python
• Good CS fundamentals in data structures and algorithm
• Detail-oriented, well organized and self-motivated with a continuous drive to learn, explore and be challenged
• Work well in teams and communicate ideas clearly

Preferred Qualifications

• PhD degree in computer science, engineering, or mathematics
• 3-5 years of relevant experience in building deep learning solutions for computer vision problems
• Hands-on experience with state-of-the-art object detection (e.g., RetinaNet, Mask RCNN, CenterNet), semantic segmentation (e.g., U-Net, deeplab), and image classification models (e.g., ResNet, DenseNet).
• Track record of publications in CV and medical image analysis is a plus
• Hands-on experience with model optimization (e.g., network quantization and mixed-precision training) is a plus
• Prior experience with medial images is a plus​​​​​​

Additional Information

We Offer…  

• An outstanding start-up culture; 
• Transparent, collaborative work environment;
• Competitive compensation
• Excellent Medical, Dental, and Vision coverage
• 401k, paid Vacation and Holiday

All your information will be kept confidential according to EEO guidelines.