What does a Machine Learning Engineer do?
Machine learning engineers work in the artificial intelligence industry. They collaborate with data scientists, computer engineers and developers to create programs that allow machines to function without direct human assistance. The machine learning engineer creates models that ensure ML programs work according to requirements. They must be proficient in data science, statistics and software engineering. ML engineers create AI algorithms for social media, online stores, Internet of Things (IoT) products and self-driving cars among others.
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Working as a Machine Learning Engineer
A Machine Learning Engineer's work depends on the product, the employer and the project including:
- Design, build and maintain machine learning systems
- Study, research and transform data science models into machine learning algorithms and tools
- Develop machine learning applications to user requirements and company specifications
- Perform machine learning tests and experiments to ensure data models translate to real-life functionalities
- Train and retrain machine learning and deep learning systems for improved performance
How much does a Machine Learning Engineer make in the United States?
Average base salary
The average salary for a machine learning engineer is $110,673 per year in the United States. 811 salaries reported, updated at August 10, 2022.
Where can a Machine Learning Engineer earn more?
Compare salaries for Machine Learning Engineers in different locationsHow much do similar professions get paid in United States?
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Common questions about for a Machine Learning Engineer
Can machine learning engineers work from home?
Machine learning engineers can sometimes work from home, depending on their current work assignments and company policy.
Is machine learning better than data science?
Machine learning is within the broader data science industry. Data scientists and machine learning engineers work together on projects and they may even switch roles. Knowledge of data science can give you an edge as it covers more functions in the bigger data processing life cycle.