We believe that the hiring process should be focused on discovering strengths, not uncovering weaknesses. It's one of the reasons we've created a variety of assessments that cover a wide range of content areas -- you can show off what you know well! However, we also know there's often overlap between engineering subfields and disagreement over which skills are associated with particular subfields. That can make it difficult to know exactly which quiz track to take when you come to Triplebyte.
Right now, scoring well on a quiz moves you forward on that track, removing your ability to take other tracks. So, even if you’re curious about several subfields, we recommend that you take either the track that covers the content you are most familiar with, or the track that aligns best with the subfield you are looking for a job in. This will help you show off your strongest tech skills and find the roles you’re most interested in.
All of our quizzes include some CS content, and each contains at least some coding. Here's a breakdown of the specialized skills and knowledge tested in each of our tracks:
This is our oldest and broadest track. It covers a broad array of topics, and emphasizes the back-end of web applications. This quiz is a good option for engineers familiar with full-stack engineering, including strong knowledge of the back-end. Those without any professional engineering experience may want to consider the entry-level version of our Generalist quiz.
This track focuses on in-browser front-end web development in modern frameworks like React and Angular. It also includes some back-end content in order to cover front-end-focused full-stack engineers. Those without any professional engineering experience may want to consider the entry-level version of our Front-end quiz.
We have mobile tracks for both iOS and Android, each of which emphasize native app development on their respective platform (not systems programming). They cover core concepts of native development on each platform, everyday UI components, and the basics of the associated languages (Objective-C/Swift or Java/Kotlin).
This track covers the ability to implement ML models in code and troubleshoot them in production. It emphasizes practical understanding of ML systems in production over underlying theory. Topics include model design, hyperparameter tuning, and common sources of error in production ML.
This track covers statistical literacy and data-analysis skills. It also includes knowledge of common data libraries like pandas and NumPy.