Software Engineer 2- Machine learning

Overview

• BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
• Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
• Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering).
• Understand machine learning principles (training, validation, etc.)
• Knowledge of data query and data processing tools (i.e. SQL)
• Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning).
• Software engineering fundamentals: version control systems (i.e. Git, Github) and workflows, and ability to write production-ready code.
• Mathematics fundamentals: linear algebra, calculus, probability
• Interest in reading academic papers and trying to implement state-of-the-art experimental systems
• Experience using deep learning architectures
• Experience deploying highly scalable software supporting millions or more users
• Experience with GPU acceleration (i.e. CUDA and cuDNN)
• Experience with integrating applications and platforms with cloud technologies (i.e. AWS and GCP)
• Experience with NLP and NLU
  

• Model Prototyping: The ML Engineer would be expected to build prototype models alongside data scientists. This may involve data exploration, high-performance data processing, and machine learning algorithm exploration. The ML engineer will be expected to come up with a rationale for model choice and come up with metrics to properly evaluate models.
• Model Productionalization: Works with data scientists to productionalize prototype models to the point where it can be used by customers at scale. This might involve increasing the amount of data used to train the model, automation of training and prediction, and orchestration of data for continuous prediction. The engineer would be expected to understand the details of the data being used and provide metrics to compare models.
• Model Enhancement: Work on existing codebases to either enhance model prediction performance or to reduce training time. In this case you will need to understand the specifics of the algorithm implementation in order to enhance it. This enhancement could be exploratory work based off a performance need or directed work based off of ideas that other data science team members propose.
• Machine Learning Tools: The ML Engineer would build a tool for a specific project, or multiple projects though generally these types of projects are decoupled from any one project. The goal of this type of use case would be to ease a pain point in the data science process. This may involve speeding up training, making a data processing easier, or data management tooling.