Responsibilities:

- Training machine learning models over billions of data points. Quantifying predictive uncertainty using probabilistic and Bayesian methods. Creating models that quickly generalize to new tasks using few-shot and meta- learning.

- Training agents that execute decisions to optimize a reward over time. Implementing state-of-the-art model-based planning and reinforcement learning algorithms, including offline and off-policy methods that learn from human demonstrations.

- Scaling machine learning systems to massive datasets using big data technologies such as Spark and Hadoop.

- Building visualization and data exploration tools that automate the analysis and debugging of machine learning models.

Requirements:

- 5+ years working experience

- Masters or PhD in computer science, or equivalent.

- Proficiency with the Python machine learning stack, including numpy, scipy, pandas, scikit-learn, matplotlib, tensorflow, keras, pytorch (not all required, more the merrier).

- Bonus points for Spark, Hadoop!