Being an ML engineer

This is my current understanding of the expectation of a machine learning engineer with a few years of working experience.

I joined my current team when it was still in a very early stage so that I got chances to practice solving different tasks under different situations/the skillsets needed for the role. Later on, the team grows. When all people in the team are familiar with everything, what makes me competitive? Why am I the person working on this project, not others? Who will I be after a few years of working as a machine learning engineer?

I read through the ml lead or senior/staff ml engineer's job description, also some daily work/shares from them. I think the required qualities include,

• to anticipate the resources required, potential issues, needs, and be able to set tasks and evaluate results

• be able to resolve issues or get resources to do that/someone who can take end-to-end responsibility.

• share takeaways in public and contribute to the community

Also, a pitfall is that no one is irreplaceable. Without one, everything will still be on. It doesn't mean the person is not important. I hope, with me, the solution is great and I also enjoyed the process.

General principle to practice:

• Replicate papers

• Don't get comfortable in the comfort zone. If you start a new project, it had better to be learn some new frameworks/libraries/tools.

• Learn boring things, like a proper Git flow, how to use Docker, how to build an app using Flask, and how to deploy models on AWS or under-appreciated by a solid majority of applicants.

• Do annoy things, like present work in the meetup, attend conferences and network etc.

• Do things that seem crazy.