Job Description

Location: San Francisco, USA
Schedule: Full time

Machine Learning Engineer

What is Machine Learning?

Machine learning is a form of artificial intelligence that enables a computational system to “learn” by analyzing large amounts of data, rather than by being programmed to follow a specific set of routines. Engineering the algorithms that allow machines to learn is not a simple process and it’s highly iterative.

What is a Machine Learning Engineer?

Machine learning engineers are critical to the success of Happy Elements. Our machine learning engineers create, refine and perfect the data mining models and algorithms machines use to analyze data and refine them based on the system outputs.

What are the responsibilities of a Machine Learning Engineer?

As a machine learning engineer at Happy Elements, you will:

• Build, maintain, and improve efficient and reliable data mining and machine learning models.
• Work closely with researchers in designing and implementing and tuning machine learning models, and provide performance feedback of machine learning models.
• Work closely with data engineers to adapt and improve data pipelines for production models.
• Work closely with software engineers in putting models into production (interface, SLA, scalability)

To be eligible for consideration at Happy Elements, your qualifications must include:

• MS in Computer Science with 2+ years of industry experience or
• PhD with 1+ years of industry experience
• Expert in Python, and computation graph toolkits (e.g., Scikit-learn, Tensorflow).
• Solid experience with Python packages such as Numpy, Panda, and Scikit-learn.
• Expert/Master skill levels in common families of machine learning models, feature engineering, feature selection techniques, and tuning of machine learning models
• Master level skills with SQL or other relational databases.
• Master level skills in building and productionizing end-to-end machine learning systems
• Knowledge and experience in cloud computing is not required but is a plus

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