Machine Learning Engineer vs. Software Engineer: What are the differences?
In the world of computer science, there are two highly sought-after professions: machine learning engineer and software engineer. These roles are at the forefront of creating innovations that shape our daily lives, including applications, websites, and software.
While there are some overlapping elements between these two positions, they each have distinct responsibilities and require different skill sets and interests. Today, Sertis aims to introduce you to these two careers, highlighting their roles, differences, and potential for collaboration. Our goal is to inspire and provide options for those seeking to expand their career paths and explore new possibilities in order to stay at the cutting edge of the modern world.
What does a machine learning engineer do?
A machine learning engineer’s main task is building machine learning models, a branch of artificial intelligence that can independently learn and process data to generate results based on specific commands. For instance, we can train a model using weather data and instruct it to forecast future weather conditions.
The primary responsibility of a machine learning engineer is to design and develop machine learning models capable of autonomously learning and processing data to achieve predefined goals. The development process begins with designing the model architecture, selecting appropriate algorithms and frameworks, training the model using structured data, testing its performance, and fine-tuning it to ensure that it is ready for deployment in software or applications.
Essential skills for a machine learning engineer
- Strong foundation in mathematics and statistics
- Proficiency in machine learning algorithms and frameworks
- Data processing and organization
- Model deployment and fine-tuning
What does a software engineer do?
A software engineer is an engineer responsible for developing software with a defined purpose and making it usable. The tasks of a software engineer encompass the entire software development lifecycle (SDLC), starting from designing and developing to updating software.
Software engineers begin by gathering feature requirements from clients and analyzing these requirements. They then proceed to design the structure of the software and write code to develop it. Afterward, they deploy, test, maintain, update, and debug the software.
Proficiency in programming languages is essential for a software engineer. They need to be adept at selecting the most suitable language, platform, and architecture to ensure that the software functions properly.
Essential skills for a software engineer
- Proficiency in programming languages such as Java, C++, Python
- Problem analysis and solving skills
- Understanding of software development life cycle and system design
- Debugging and testing
Machine learning engineer vs. Software engineer: what are the differences?
The distinct differences between these two roles lie in their primary focuses and the outcomes they produce. A machine learning engineer primarily focuses on developing and training machine learning models, while a software engineer focuses on software development in general. Each role is responsible for different types of creations.
Machine learning engineers work on developing and training models, and fine-tuning algorithms and data to achieve desired results. They often require an open-minded mindset to experiment and iterate on their models. On the other hand, software engineers focus on writing code that enables software to function properly, requiring a more straightforward and systematic mindset.
For instance, when we receive a notification from our email software warning us that an email from an unknown address may be spam and giving us the option to approve or reject it, this functionality was built by a software engineer who wrote the code to generate alerts when unknown addresses are detected. On the other hand, if our email software automatically detects and blocks addresses at risk of being spam without any user intervention, this functionality is enabled by a machine learning engineer who has trained a model on vast amounts of data to automatically protect us from spam.
Machine learning engineer and software engineer: the collaboration
Although these two roles are distinctively different, they can collaborate to deliver efficient and practical work. a software engineer is responsible for developing software or applications that incorporate the model developed by a machine learning engineer. This integration allows the software to function in a human-like way and deliver more impressive results.
For instance, when developing a robot, a software engineer would build the robot and write the code that instructs the robot on how to move its arms and legs to walk. Meanwhile, a machine learning engineer would develop a model that serves as the robot’s ‘brain,’ teaching it how to think, interpret conversations, and respond appropriately.
The collaboration between these two roles and other roles is crucial in achieving intelligent and sophisticated robots that work efficiently.
At Sertis, we believe in bringing together experts from different fields to create top-performing innovations in a wide range of areas. We think that working together and sharing knowledge is crucial for success. Every solution we deliver to clients is the result of collaboration among all of us. All Sertizens have the chance to gain experience in their own areas of expertise and learn from others through collaboration. They also get to learn from clients from different industries in Sertis’ lifelong-learning environment.
Find open positions and job opportunities at: https://www.careers.sertiscorp.com/jobs
