ML Systems/AutoML Engineer (Center of Integrative Artificial Intelligence)
We are looking for talented, motivated full-time ML Systems and AutoML (automated machine learning) Engineers who can deliver consistently in a fast-paced and high-quality manner. You will be responsible for helping build robust, effective, and well-packaged modern ML-Systems and AutoML systems, as well as contributing to our open-source projects.
- Collaborate with system architects, designers, and engineers to support the development of robust machine learning systems.
- Contribute high-quality code and lead efforts in building open-source projects
- Develop parallel programming techniques to simplify distributed ML programming.
- Learn and implement state-of-the-art deep AutoML algorithms to support tasks such as hyperparameter optimization, neural architecture search, data augmentation, feature engineering, and more.
- Assess and recommend technology choices and directions in consideration of cost-benefit trade-offs.
- Communicate your work to a broader audience through talks, tutorials, and blog posts.
- 2+ years of experience in one or more areas listed below:
- AutoML areas such as hyperparameter tuning, architecture search or manual design, data preparation, augmentation, or feature engineering
- Distributed systems
- Network communication, or storage systems
- Hands-on experience with at least one popular deep learning framework such as PyTorch and Tensorflow.
- High-level engineering skills in Python and C++.
- Master’s degree in Computer Science, Machine Learning, or related fields with 2+ years of industry/research experience, or Ph.D. degree in Computer Science, Machine Learning, or other relevant degrees.
- Experience with model-based optimization (e.g. Bayesian optimization) methods or software frameworks.
- Experience in deploying machine learning algorithms in resource-restricted environments such as mobile or embedded systems.
- Experience in developing with Docker, Kubernetes, Ray, NNI, etc.
- Experience in contributing to notable open-source ML software, such as TensorFlow, PyTorch, etc.
- Publication (or submission) of a paper to machine learning or operating systems conferences.