The Machine Learning Software Engineering Interview
&gt; In the context of the modeling onsite, we ask open-ended problems with sufficient business and problem context such that the candidate can clearly identify an ML-based approach to solve it.<p>I&#x27;m disappointed the author wasn&#x27;t more specific about where the line is drawn between &quot;ML SWE&quot; and &quot;Research Scientist&quot;&#x2F;&quot;Data Scientist&quot; when it comes to the core ML competencies like model selection, evaluation, and design.<p>Having worked in teams with Data Scientists and &quot;ML Engineers&quot; it&#x27;s been murky whether me and others in my team that were not the former were Data Engineers, Backend Software Engineers, &quot;ML SWE&quot;, or &quot;Software Engineer - Machine Learning&quot;.<p>This area of software is rapidly developing and there&#x27;s no semblance of the bright line that tends to exist between Backend Engineer and Frontend Engineer for ML-involved engineers.<p>I&#x27;m personally interested in moving into the &quot;Software Engineer - ML&quot; space (or is it &quot;ML SWE&quot;?) and thus I have to find out what the right balance between Software Eng skills and researcher skills is. I think I have a decent sense of 
real-world ML basics but would quickly flounder when pressed for details on mathematical technicalities of different modelling approaches.<p>Lyft&#x27;s job listings have these items for &quot;Software Engineer - Machine Learning&quot;:<p>&gt; 5+ years (or Ph.D. with 2+ years) of industry or research experience developing ML models<p>This really seems like the purview of a research scientist or a data scientist, if I understand the meaning of &quot;developing&quot; correctly.<p>&gt; Proven ability to quickly and effectively turn research ML papers into working code<p>This seems fair enough, but my experience has been that the data scientists are doing this mostly, while the &quot;Software Engineer - ML&quot; people are preparing data pipelines or batch training systems.<p>&gt; Deep knowledge of ML libraries like scikit-learn, Tensorflow, PyTorch, Keras, MXNet, etc<p>I know it&#x27;s a job ad and thus it is a &#x27;wish list&#x27;, but this seems unreasonable.<p>Guess I&#x27;ll just keeping studying &#x27;all the things&#x27;. Brb in 5 years.