What does a Machine Learning Engineer do?
Machine Learning Engineers

Discover the role, main responsibilities, and skill set of our top-notch remote machine learning engineers.

Main Responsibilities of a Machine Learning Engineer

Explore the key roles and responsibilities that our Machine Learning Engineers undertake to ensure the smooth operation of your data infrastructure.

Top Responsibility
Algorithm Development

Machine Learning Engineers develop and implement algorithms that are used in adaptive systems.
Machine Learning

Machine Learning Engineers build predictive models and machine-learning algorithms.
Data Management

Machine Learning Engineers ensure data quality and integrity.
Strategy Planning

Machine Learning Engineers use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
Strategy Planning

Data Scientists use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.

The typical tech stack managed by a Machine Learning Engineer might vary based on the project's requirements.

These are referential prices based on the average cost of hiring a Machine Learning Engineer at different levels of expertise.

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	Data analyst .	Data Engineer .	Data Scientist .	ML Engineer .
Data Collection	Collects and processes raw data	Designs, constructs, installs, tests and maintains data management systems	Does not typically collect raw data	Does not typically collect raw data
Data Analysis	Analyzes and interprets complex data sets	Ensures systems meet business requirements and industry practices	Interprets trends and patterns in complex data sets	Interprets trends and patterns in complex data sets
Data Modeling	Does not typically build predictive models	Builds algorithms and prototypes	Builds and optimizes data models	Designs and builds machine learning models
Machine Learning	Does not typically work with machine learning	Does not typically work with machine learning	Builds predictive models and machine-learning algorithms	Builds predictive models and machine-learning algorithms
Data Visualization	Presents data in a visual context	Does not typically present data visually	Presents data in a visual context	Presents data in a visual context
Data Cleaning	Cleans and validates data for analysis	Cleans and validates data for storage	Cleans and validates data for analysis	Cleans and validates data for machine learning models
Data Integration	Does not typically integrate data from different sources	Integrates data from different sources	Cleans and validates data for analysis	Cleans and validates data for machine learning models

Data analyst .
Data Collection Collects and processes raw data
Data Analysis Analyzes and interprets complex data sets
Data Modeling Does not typically build predictive models
Machine Learning Does not typically work with machine learning
Data Visualization Presents data in a visual context
Data Cleaning Cleans and validates data for analysis
Data Integration Does not typically integrate data from different sources

Data Engineer .
Data Collection Designs, constructs, installs, tests and maintains data management systems
Data Analysis Ensures systems meet business requirements and industry practices
Data Modeling Builds algorithms and prototypes
Machine Learning Does not typically work with machine learning
Data Visualization Does not typically present data visually
Data Cleaning Cleans and validates data for storage
Data Integration Integrates data from different sources

Data Scientist .
Data Collection Does not typically collect raw data
Data Analysis Interprets trends and patterns in complex data sets
Data Modeling Builds and optimizes data models
Machine Learning Builds predictive models and machine-learning algorithms
Data Visualization Presents data in a visual context
Data Cleaning Cleans and validates data for analysis
Data Integration Cleans and validates data for analysis

ML Engineer .
Data Collection Does not typically collect raw data
Data Analysis Interprets trends and patterns in complex data sets
Data Modeling Designs and builds machine learning models
Machine Learning Builds predictive models and machine-learning algorithms
Data Visualization Presents data in a visual context
Data Cleaning Cleans and validates data for machine learning models
Data Integration Cleans and validates data for machine learning models

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