D Species of ML Engineer, and skilling broad vs deep
ML Engineer is inherently an "in-between" job - not quite a DS, not quite a developer, jack of all trades. AFAICT, different companies have different ideas about what an MLE is for. These are the different "species" I have seen in job ads:
Data engineer by another name: Spark/Hadoop/Glue, DB & data warehouse stuff, ETL etc.
Researcher plus: lots of experience in a specific area (typically DL, NLP, CV etc.), combined with some low-level skills in C/C++
ML Ops/DS+Dev: DS background but focused on implementation & deployment - needs production-level code, plus skills in devops stuff (K8s/CI/CD tools/etc.) and/or backend stuff (e.g. Redis, Kafka, an AWS or GCP cert)
Hardcore low-latency ninja: seen especially in financial services, really strong low-level skills in a C-family language or similar - presumably working on implementation of DL at the edge where speed is key
No two jobs are the same, of course, but most I have seen appear to fall into these broad categories.
What do you think? Is this a fair assessment?
Do you think an MLE should be focused on specialising highly into one of these areas, or is a broad, flexible skill set equally useful?
(NB I originally posted this as a direct question in cscareerquestions, but it doesn't fit their developer-focused subreddit. I'm hoping it can be allowed as a potentially interesting discussion)
/r/MachineLearning
https://redd.it/latkui
