Senior Machine Learning Engineer, Core Modeling
As a Machine Learning Engineer on our Core Modeling team, you will work on DataRobot’s machine learning platform and actively contribute to the development of our state-of-the-art preprocessing and modeling capabilities.
Core Modeling owns the entire data science backend for DataRobot, and is responsible for making sure our models and modeling automation are the best in the world.
We are looking for talented people with excellent engineering skills and deep knowledge of machine learning who can analyze problems, design unprecedented solutions, and implement them for real-world use on top of our platform.
DataRobot is based around delivering best-in-class AI solutions, and this position provides the opportunity to build the key machine learning components of our system.
- Automate machine learning processes
- Design and build machine learning models for accuracy and scalability
- Integrate machine learning algorithms with other applications and services
- Recommended background: 5+ years of combined Python engineering and machine learning experience
- Experience writing maintainable, testable, production-grade Python code
- Understanding of different machine learning algorithm families and their tradeoffs (linear, tree-based, kernel-based, neural networks, unsupervised algorithms, etc.)
- Good command of scientific Python toolkit (numpy, scipy, pandas, scikit-learn)
- Understanding of time, RAM, and I/O scalability aspects of data science applications (e.g. CPU and GPU acceleration, operations on sparse arrays, model serialization and caching)
- Software design and peer code review skills
- Experience with automated testing and test-driven development in Python
- Experience with Git + GitHub
- Comfortable with Linux-based operating systems
- Experience with large-scale machine learning (100GB+ datasets)
- Experience with deep learning libraries and frameworks (TensorFlow, Keras, PyTorch etc.)
- Competitive machine learning experience (e.g. Kaggle)
- Previous experience of deploying and maintaining machine learning models in production