I'm not sure if the machine learning engineer role is very well-defined. IMO, a good MLE does "full-stack" work -- owning ML end-to-end, from model development to integration in production pipelines.
I interview for both MLE and data science roles. Here's what I look for:
I interview for both MLE and data science roles. Here's what I look for:
"Strongly suggested" languages:
MLE: Spark or Hadoop (or some ETL experience), Python
DS: Python, Pandas (or some datafame manipulation experience)
R can substitute for Python, but in tech it's hard to get used to full Python workflows & collaborate without some Python exp.
MLE: Spark or Hadoop (or some ETL experience), Python
DS: Python, Pandas (or some datafame manipulation experience)
R can substitute for Python, but in tech it's hard to get used to full Python workflows & collaborate without some Python exp.
Technical concepts:
MLE (more programming and design-heavy): OOP -- do you know what a class is? Can you write good abstractions? Can you design basic DB schemas? How do you debug? Can a DS easily understand your Python code? Do you know basic models and how to write baselines?
MLE (more programming and design-heavy): OOP -- do you know what a class is? Can you write good abstractions? Can you design basic DB schemas? How do you debug? Can a DS easily understand your Python code? Do you know basic models and how to write baselines?
Technical concepts:
DS (more stats-heavy): basics (GLMs, trees, parametric vs nonparametric), strengths and weaknesses of different models, dimensionality reduction, ability to "think critically" about data -- how would you "clean" it? What's the right metric for the problem?
DS (more stats-heavy): basics (GLMs, trees, parametric vs nonparametric), strengths and weaknesses of different models, dimensionality reduction, ability to "think critically" about data -- how would you "clean" it? What's the right metric for the problem?
I look for some machine learning experience:
Tell me about a machine learning project you worked on. FYI -- one machine learning project is more than enough. I care more about depth in a project rather than you doing 12 different Kaggle projects that lasted a few days each.
Tell me about a machine learning project you worked on. FYI -- one machine learning project is more than enough. I care more about depth in a project rather than you doing 12 different Kaggle projects that lasted a few days each.
I look for some "independent thinking":
What if [x] assumption didn't hold in your project -- how would you modify the algorithm or pipeline? One can always poke some hole or next step in even the best projects. Can we brainstorm solutions to these issues together?
What if [x] assumption didn't hold in your project -- how would you modify the algorithm or pipeline? One can always poke some hole or next step in even the best projects. Can we brainstorm solutions to these issues together?
I look for an ability to perform "good science":
How do you iterate on experiments? How do you share results / get buy-in? Big red flags: you change multiple variables at once, you don’t think carefully about metrics, you don’t keep track of your experiment artifacts, etc.
How do you iterate on experiments? How do you share results / get buy-in? Big red flags: you change multiple variables at once, you don’t think carefully about metrics, you don’t keep track of your experiment artifacts, etc.
I don't want to hire textbooks:
Do you have actual opinions on technologies or tools? How do you use them? Ex: deep learning classifiers are bad at imbalanced data but you had lots of data, so you fit a boosted tree classifier on features identified by a deep learning model.
Do you have actual opinions on technologies or tools? How do you use them? Ex: deep learning classifiers are bad at imbalanced data but you had lots of data, so you fit a boosted tree classifier on features identified by a deep learning model.
I look for people who are eager to learn:
What’s the last paper or technical blog post you read that you found interesting? Explain it to me. Tell me what’s really excites you about it. I am a pretty excitable person, so it shouldn't be too much work to get me excited :)
What’s the last paper or technical blog post you read that you found interesting? Explain it to me. Tell me what’s really excites you about it. I am a pretty excitable person, so it shouldn't be too much work to get me excited :)
Finally, I look for people who I could enjoy working with, and people who could enjoy working with me:
Do you like collaborating with other people, or do you prefer being siloed? Did this conversation energize you, or did you find it annoying?
Do you like collaborating with other people, or do you prefer being siloed? Did this conversation energize you, or did you find it annoying?
IMO, depth in only one thing (ex: deep learning) doesn't matter too much. I think good science and thoughtfulness are much more important. The goal at a startup isn't to be a solo 10x engineer/scientist. It's to be a good team player and build a technical culture that lasts.
