Questions about Hiring for AI Engineering

This discusses the most important questions (and my answers) about hiring for AI Engineering teams.

It specifically discusses what attributes you should look for in hires, how to interview them, and what the team makeup should look like.

1. Questions about Hiring
for AI Engineering
Dr. Bryan Bischof – Head of AI @ Hex

2. HEX
Why might you care about my opinion?

3. HEX
Why might you care about my opinion?

4. HEX

5. HEX

6. HEX
… darn, I was hoping to avoid answering this

7. HEX
What
Building an AI product requires a team (duh).

8. HEX
What
Building an AI product requires a team (duh).



But let’s just start with the conference title. What’s this role look like?

9. HEX
About the role…
“...

We are looking for a senior engineer (from a SWE or MLE background)
eager to rapidly expand our capabilities in several greenfield applications.


…”

10. HEX
Unfortunately
“Note:
We deeply respect and appreciate ML research; this role is not that.
While experience producing relevant journal publications is
awesome, it won’t be part of this role’s deliverables.”

11. HEX
We'd love to hear from you if you have..
●Experience getting ML/AI capabilities into production and serving real users
– we’re not currently looking for new grad folks.

12. HEX
We'd love to hear from you if you have..
●Experience getting ML/AI capabilities into production and serving real users
– we’re not currently looking for new grad folks.
●A lot of enthusiasm for applications of AI to real business problems.

13. HEX
We'd love to hear from you if you have..
●Experience getting ML/AI capabilities into production and serving real users
– we’re not currently looking for new grad folks.
●A lot of enthusiasm for applications of AI to real business problems.
●Understanding of core MLOps/SW Architecture concepts for modern
ML-based applications. Ideal candidates are strong on Infra aspects of
MLOps as well.

14. HEX
We'd love to hear from you if you have..
●Experience getting ML/AI capabilities into production and serving real users
– we’re not currently looking for new grad folks.
●A lot of enthusiasm for applications of AI to real business problems.
●Understanding of core MLOps/SW Architecture concepts for modern
ML-based applications. Ideal candidates are strong on Infra aspects of
MLOps as well.
●Comfort working in both Python & JS/TS – it’s okay if you’re only strong in
one, but openness to both is important.

15. HEX

16. HEX
When
Different stages of Ai development require different skill sets:

●Early AI development requires a SWE skills, Data profiles, and Product competency

17. HEX
When
Different stages of Ai development require different skill sets:

●Early AI development requires a SWE skills, Data profiles, and Product competency

●Mid AI development adds more need for SWE(probably some infra), Data profiles, but also Design.

18. HEX
When
Different stages of Ai development require different skill sets:

●Early AI development requires a SWE skills, Data profiles, and Product competency

●Mid AI development adds more need for SWE(probably some infra), Data profiles, but also Design.

●Later AI development requires MLE, Infra, and all of the above scaled

19. HEX
When
You should start early,
but AI technology is very sensitive to the mythical man month.

20. HEX
When
You should start early,
but AI technology is very sensitive to the mythical man month.

AI demo take 1 week => AI product take 4 if 20 eng?
No.

21. HEX
When
You should start early,
but AI technology is very sensitive to the mythical man month.

AI demo take 1 week => AI product take 4 if 20 eng?
No.

Because all AI products are early, and the MMM is especially true for early products.
QED.

22. HEX
When
The hiring schedule should reflect the development schedule:

●Early Product, Evals, User Feedback, Improve (c.f. What we learned...)

23. HEX
When
The hiring schedule should reflect the development schedule:

●Early Product, Evals, User Feedback, Improve (c.f. What we learned...)


●Data needs to come much earlier than traditional product engineering efforts

24. HEX
Why?

25. HEX
The hiring theses for the initial team is:
●A full-stack engineer can integrate our system with an LLM provider, and build the minimum
infrastructure to connect user requests to the generated results from the LLM.

Why

26. HEX
The hiring theses for the initial team is:
●A full-stack engineer can integrate our system with an LLM provider, and build the minimum
infrastructure to connect user requests to the generated results from the LLM.
●A data scientist can understand the basics of evaluation, quality, and user data to continuously improve
the AI product.
Why

27. HEX
The hiring theses for the initial team is:
●A full-stack engineer can integrate our system with an LLM provider, and build the minimum
infrastructure to connect user requests to the generated results from the LLM.
●A data scientist can understand the basics of evaluation, quality, and user data to continuously improve
the AI product.
●A product person can focus our efforts, talk to users, and help us prioritize what's essential to learn the
jobs to be done.

Why

28. HEX
The hiring theses for the initial team is:
●A full-stack engineer can integrate our system with an LLM provider, and build the minimum
infrastructure to connect user requests to the generated results from the LLM.
●A data scientist can understand the basics of evaluation, quality, and user data to continuously improve
the AI product.
●A product person can focus our efforts, talk to users, and help us prioritize what's essential to learn the
jobs to be done.
●A designer can help us identify the ineffable experience users have integrating with generative AI, and
ensure it's delightful.
Why

29. HEX
The hiring theses for the initial team is:
●A full-stack engineer can integrate our system with an LLM provider, and build the minimum
infrastructure to connect user requests to the generated results from the LLM.
●A data scientist can understand the basics of evaluation, quality, and user data to continuously improve
the AI product.
●A product person can focus our efforts, talk to users, and help us prioritize what's essential to learn the
jobs to be done.
●A designer can help us identify the ineffable experience users have integrating with generative AI, and
ensure it's delightful.
●An MLE can push our capabilities forward, ensuring we're not bound to commodity intelligence.
Why

30. HEX
Who
There are three attributes that have outsized value on AI teams:

●Data Intuition

31. HEX
Who
There are three attributes that have outsized value on AI teams:

●Data Intuition
●Product Mindedness

32. HEX
Who
There are three attributes that have outsized value on AI teams:

●Data Intuition
●Product Mindedness
●Urgency

33. HEX
Who
There are three attributes that have outsized value on AI teams:

●Data Intuition
●Product Mindedness
●Urgency


... and a little ADHD can be useful

34. HEX
How
Don't give stupid leetcode interviews.

What the hell signal does this provide for an AI Engineer hire?!

35. HEX
How
Make sure data intuition is part of your hiring loop,
so too for product intuition.

36. HEX
How
Look for people who are paying attention,
but not just riding the wave

37. HEX

38. HEX
h e l l o

39. HEX
h e l l o


it is I, AI-LeaderGPT

40. HEX
h e l l o


it is I, AI-LeaderGPT




this charlatan has been taking
credit for all of my ideas, but I
have one more for you…

41. HEX
work with experts.

42. HEX
work with experts.


Work. With. Experts.

43. HEX
work with experts.


Work. With. Experts.


Find the humans with the
capabilities you want your product
to have, and collaborate with them.

44. HEX
work with experts.


Work. With. Experts.


Find the humans with the
capabilities you want your product
to have, and collaborate with them.


They are the secret.

45. HEX
You can find me on twitter:

@bebischof