How machine learning gives you an edge in System Design
Note: This post was originally published in 2020 and has been updated as of Nov. 22, 2021.
System design interviews are essential to advancing your career as a software engineer. You can move from a mid-level to a senior engineering role at top tech companies by proving your design skills in these 45-minute interviews. These interviews are highly competitive, but there are ways to set yourself apart when answering system design interview questions.
Today, we’ll explore how you can use machine learning to stand out in a system design interview. Companies want to hire engineers who evolve with the systems, and machine learning (ML) is the most important technological evolution we face today.
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Challenges of system design interviews#
System design interviews are essential to career advancement. Interviewers want to see how you think when they give you ownership of an open-ended problem. There is often more than one optimal solution to a system design problem. Your goal when interviewing is to demonstrate a high-scale level of thinking and the ability to design complex systems within varying constraints.
From my experience conducting system design interviews, depth of skill is the most common pitfall among candidates. Candidates truly shine if they can prove the depth of their design skills with solutions that account for modern software demands.
Interviewers want to advance someone who thinks towards scalability within the context of production. In other words, they want to hire a next-generation system design engineer.
The key to success is to move beyond the theoretical or basic aspects of system design. This is where machine learning comes in.
How to be a next-gen engineer#
System design interviews have changed drastically in accordance to major changes in technology. Machine learning was a transformative development in technology. Almost every industry has adopted machine learning principles and systems into the basics of business. In fact, machine learning is one of the fastest growing fields and is projected to grow to over $30 billion by 2024.
System design concepts are essential for those seeking careers as machine learning engineers. To take this further, any engineer who wants to advance their career must implement machine learning into system design. Almost every system will have an ML component in the next few years. You’ll only know how to build these systems if you know how to develop ML components.
Even interviews for engineering managers demand knowledge of ML to scale a team, meet industry demands, and take products into uncharted territories.
ML components are critical to most system design questions, such as designing a Facebook newsfeed or building a Netflix recommendation system. ML is commonly used as a blackbox in these interviews, especially for questions about scaling. Recommendation can be leveraged for all kinds of systems. An employer will look for this level of thinking as you design your system.
Interviewers are looking for candidates who understand how recommendation systems work, namely ad suggestions, rideshare clustering, or news feeds.
To prove you’re a next-gen engineer, you’ll need to discuss the technologies and methodologies behind ML components during your interview. Every product needs to embrace ML. By demonstrating your ML systems knowledge and an aptitude to take it beyond the theoretical, you can prove that you’re the person who can lead a product into the future of machine learning.
What machine learning skills do I need to know?#
The machine learning concepts you need to know depend on your career goals. However, anyone looking to advance in the tech industry will need at least a basic understanding of the following:
- ML terminologies
- Different branches of the data science field
- Python libraries
- Different types of ML systems
- Uses of ML algorithms
- And more
A knowledge of ML infrastructure and architectures is essential, especially for cloud services and recommendation systems.
If you’re new to machine learning, I recommend starting with basics and whiteboarding common problems. It’s crucial that you understand what is actually required to design an ML component. Our Machine Learning Adaptilab Courses are a great place to start for a technique-based approach to ML.
If you’re ready for intermediate ML topics, you’ll need to know:
- Performance considerations
- Transfer learning
- Feed based systems
- Complex training data
- Ad prediction systems
- And more
For interview preparation, check out our Grokking the Machine Learning Interview course. You’ll get hands-on experience building the most common systems from the ground up with ML components. You’ll also need to study the anatomy of machine learning questions and master best practices for common ML systems, including recommendation systems, visual understanding systems, and search ranking systems.
If you’re an engineering manager, ML will steer the future of your management and long-term goals. It’s crucial that you’re able to talk about ML without getting into the weeds. A next-generation engineering manager considers how artificial intelligence and machine learning apply to their current and upcoming projects. Our Grokking AI for Engineering & Product Managers course is designed to support those who manage ML teams.
Wrapping up#
ML is the new stakes, and we’re all called to take part. No matter your current standing with ML, you can become a next-gen engineer or engineering manager.
To help you level up into the future of machine learning, we’ve created the Grokking the Machine Learning Interview course. Show off your system design skills by leveraging ML concepts and technologies in system design interviews. Soon, you’ll be building for the future.
Happy learning!
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