> For the complete documentation index, see [llms.txt](https://lwang010.gitbook.io/longw/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lwang010.gitbook.io/longw/softskills/chap-4.-mentorship/find-some-role-models.md).

# 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.