Machine learning engineer vs. software engineer

ML engineering is considered a subfield of software engineering. In most organizations, the hiring process for MLEs is spun out of their existing SWE hiring process. Some organizations might swap out a few SWE questions for ML-specific questions. Some just add an interview specially focused on ML on top of their existing interview process for SWE, making their MLE process a bit longer than their SWE process.

Overall, MLE candidates are expected to know how to code and be familiar with software engineering tools. Many traditional SWE tools can be used to develop and deploy ML applications.

In the early days of ML adoption, when companies had little understanding of what ML production entailed, many used to expect MLE candidates to be both stellar software engineers and stellar ML researchers. However, finding a candidate fitting that profile turned out to be difficult, and many companies had relaxed their ML criteria. In fact, several hiring managers have told me that they’d rather hire people who are great engineers but don’t know much ML because it’s easier for great engineers to pick up ML than for ML experts to pick up good engineering practices.

🌳 Tip 🌳
If you’re a candidate trying to decide between software engineering and ML, choose engineering.

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