Senior Machine Learning Engineer

Position Overview 
Our Advanced Learning Systems team is seeking a senior machine learning engineer. The team is in need of someone who is a skilled of standard data and ML pipelines, ML system design and modeling, and optimization techniques. Deployment skills are NOT a must but a plus. As a machine learning engineer, you will be building state-of-the-art ML models for different industrial applications.

Job Duties/Responsibilities   

• Design, train and deliver machine learning solutions to a wide range of datasets: from small datasets to big data, time series to 3D matrices, both unlabeled and labeled
• Collaborate with the development team and ML engineers to implement, test and validate new algorithms and integrate the algorithms with the existing frontend and backend systems
• Develop optimization tools on top of ML models to recommend optimal operation
• (As an individual contributor) Build state-of-the-art models for various modeling tasks (e.g., time series forecasting, spatiotemporal and traffic/logistic)
• Break down problems and propose solution roadmap
• Mentor junior engineers, perform code reviews and document design decisions

 Minimum Qualifications   

• PhD or MS in Computer Science, Electrical Engineering or similar field
• 5+ years of experience with one modern coding languages, e.g., C++, Python
• 5+ years of experience with machine learning tools and deep learning algorithms and techniques, including but not limited to:
  • PyTorch, TensorFlow, Keras
  • CNN, LSTM, RNN
  • Scikit-learn, pandas, numpy
• Optimization techniques and fine-tuning models, e.g., scipy.optimize, optuna, …
• Able to work independently on problems and in highly collaborative team environments
• Team management and mentoring junior members
• Effective communication with internal and external sources
• Time and project management

Preferred Qualifications 

• Graph-based Machine Learning
• Research experience with high impact publications
• Application of ML in industrial projects
• Experience with MLOps and other production level implementation frameworks and pipelines
• Experience with messaging systems, Docker, Kubernetes, Spark, EMR, and cloud platforms (AWS or Azure)