(Opinionated) Guide to ML Engineer Job Hunting
Over 2 years ago, I wrote a blog post on how to find jobs as a new grad data scientist (as a twist of fate, I never worked as a product data scientist but instead became an ML engineer at DoorDash). Back in 2021, I cared a ton about interview skills, answer “frameworks”, and whatnot, which may still come handy at New Grad or Early Career levels. For experienced hires, however, I think of interviews as some sort of marriage proposal — it’s something you can rehearse but can never force.
If your resume is strong enough, you may get a date or two (e.g., recruiter call/HM chat/phone screen), but for the company to say “yes” (the offer), the expertise you bring to the table has to fit their roadmap and the person that you are must fit the company/team culture — otherwise, the marriage will be painful (and short).
Of course, you can and should prepare for interviews — just like people spent days or even months preparing for the very moment to propose, but a strong foundation should’ve already been built over the years through thick (ML counterpart: when your models succeed) and thin (ML counterpart: when you learn from failures).
