Data science has been one of the fastest-growing occupations over the past few years. According to BLS, around 11.5 million new jobs will be created in this field as demands are increasing in data science and related technical positions.
Machine learning engineer is among these top emerging careers in the data science complex that was named the best job in the US for 2019. So if you want to start on a career path in this in-demand field, here we’ve covered the basics of the machine learning engineer role, including the responsibilities, skills, salary, and more
Let’s start with the most basic questions:
What is a machine learning engineer?
Machine learning engineers combine data science and software engineering to build AI systems. The ML engineer receives the AI model from the data scientist or deep learning specialist and implements it into production. In other words, they are the bridge between data models and the software, creating self-running programs and algorithms capable of learning without the need for further programming.
What do machine learning engineers do?
As we’ve already described above, machine learning engineers design AI systems to automate predictive models for virtual assistants, chatbots, recommended searches, translation apps, driverless cars, and other technologies. So their responsibilities may vary depending on the type of the project they are working on. For example, a machine learning specialist who works with virtual assistants would mainly focus on speech recognition technologies, natural language processing and machine translation. Meanwhile, machine learning engineers who work on self-driving car technology focus on computer vision and reinforcement learning.
Let’s go through the main roles and responsibilities of a machine learning engineer.
- Design and develop machine learning systems using algorithms, data structures, as well as computer architecture
- Perform computations and statistical analysis
- Deploy and monitor ML software
- Implement ML algorithms and tools
- Build data and model pipelines
- Manage the data sets and data pipelines needed to bring code to production
- Research and apply the best practices for improving the existing ML infrastructure
What is a machine learning engineer’s salary?
Machine learning jobs are among the highest-paying with a rather competitive average salary. Based on the skills, experience level, location, and company, the average salary range among machine learning engineers is broad, ranging anywhere from $76000 to over $154000, according to PayScale and Glassdoor.
As PayScale data company reports, junior-level machine learning specialists who have a graduate degree and up to 3 years of experience can expect a salary of around $119000. Mid-level machine learning engineers with between 5 to 9 years of experience in the industry earn on average around $137,685. Senior machine learning engineers that boast over 10 years of experience can get as high as $165,000. Let us note that this is the average approximate salary for this role, and salaries for ML engineers may vary significantly across the countries, depending on the industry, company, experience, and other factors.
What skills do you need to become a machine learning engineer?
The skills and qualifications for machine learning jobs may differ upon companies. Here are the skills and qualities to look for in the first place:
Strong mathematical and statistics skills
- Linear algebra to perform computations and work with algorithms
- Calculus to get a better understanding of how machine learning models work
- Statistics and probability to analyze past events and calculate the likelihood of future events.
- Experience in programming languages, such as Python, Java, Scala, or C++
- Knowledge of R, Prolog, and Lisp
- A good understanding of computer infrastructure
- Skills in data modelling and data architecture
- Ability to work with massive datasets and extract valuable insights from big data
- A good understanding of computer infrastructure
- Familiarity with visualization tools such as Tableau, Dash, or Power BI
Proficiency with ML frameworks and libraries
- Experience in working with ML frameworks (TensorFlow, PyTorch) and ML libraries (Scikit learn, Theano, Matplotlib, etc.)
- Knowledge of big data frameworks (Hadoop, Hive, Flume, Spark, etc.)
- A strong understanding of deep learning and neural networks
Excellent communication, problem-solving and time management skills to be able to
- find different solutions to fix bugs in ML models
- explain complex ML concepts to people who have little or no expertise in the field
- collaborate effectively with data scientists, data engineers, software engineers and more
Want to learn machine learning? Check out our learning resources for data scientists and ML engineers.
What jobs are similar to a machine learning engineer role?
Jobs similar to a machine learning engineer role in a data science team include data scientists, data engineers and AI engineers.
Machine learning engineer vs data scientist
While the functions of a machine learning engineer and data scientist may overlap, these specialists are responsible for different parts of an ML project. Data scientists follow the data science process, they carry out experiments to understand data and build models. Then they evaluate the model and make sure it meets the desired outcome of the project before handing it over to ML engineers.
Machine learning engineers are tech specialists who create and maintain ML infrastructure upon which models are deployed to a production environment. They take the models built by data scientists and make them work in a production environment where the model will be accessible to your computers, phones and other software systems.
Machine learning engineer vs data engineer
While machine learning engineers are responsible for delivering the ready-to-use AI models, data engineers design the whole data architecture and the application logic to process the data. In other words, data engineers are specialized in data pipelines converting raw data into a useful format for analysis and making sure that data flows as required for the models to actually work.
Machine learning engineer vs AI engineer
AI engineers develop and program algorithms that make up machines capable of functioning like a human brain. The role requires a sound understanding of programming, software engineering, data science and data engineering. They build and test AI models and then use API calls or embedded code to create AI applications.
Whether you’re just starting to explore or have already committed to a career in machine learning, we hope this guide will help you get a better understanding of a machine learning engineer role and move forward in this field.
By Siranush Andriasyan
SmartClick is a full-service software provider delivering artificial intelligence & machine learning solutions for businesses.