- Research and implementation of novel/advanced deep neural network architectures and learning techniques to solve a variety of computer vision tasks and push the state of the art in performance.
- The topics may include but not limited to: object/key-point detection, semantic segmentation, face attributes/landmark/recognition, video understanding/behavior modeling, and efficient architecture.
- Problem formulation, benchmarking, dataset analysis/collection, designing and finetuning model with required accuracy and speed.
- Build and maintain the infrastructure for training and deploying models, including data pipelines, experiment management platform, visualization tools, etc.
- Integration of model components with the product stack.
- MS / PhD in Computer Science, Electric Engineering, and/or Artificial Intelligence, Machine Learning related technical field with 2+ years of deep learning experience on computer vision.
- 3-5 years of software engineering experience in an academic or industrial society.
- Holistic understanding of deep learning concepts, state of the art in computer vision research and the mathematics of machine learning.
- Proficiency in at least one of the popular computational and deep learning frameworks, such as Pytorch, TensorFlow, Keras, etc.
- Proficiency in Python and C/C++.
- Proven track record of high-quality engineering output (side projects, internships, research projects, full-time jobs etc.).
- The ability and desire to work in the dynamic environment of an early-stage company.
- Research experience in Algorithms, Architecture, Artificial Intelligence, Data Mining, Distributed Systems, Machine Learning, Networking, or Systems.
- A solid foundation in computer science, with competencies in data structures, algorithms, and software design.
- Strength on creativity and communication.