Landing an ML Job- secrets from technical recruiters

Welcome to Landing an ML Job- a virtual Learner Community Event hosted by DeepLearning.AI and Workera! A panel of technical recruiters will be sharing suggestions on potential career paths as well as tips and best practices for interview prep.

Agenda: PDT (*subject to change)

MC: Sandhya Simhan, Director of Marketing, DeepLearning.AI

5mins: Opening speech: Kian Katanforoosh, Founder & CEO, Workera

40mins: Panel discussion:

Moderator: Kian Katanforoosh, Founder & CEO, Workera

Panelists:
-Ebitie Amughan, Technical Recruiter, Pinterest
-Lawrence Gomez, Technical Recruiter, Upstart
-Dana Schafer, Technical Recruiter, Grammarly
-Melisa Tokmak, Head of Document Products, ScaleAI

15mins: Q&A. We will be taking questions from our learners in the Slack community. If you’re currently taking the AI for Medicine, NLP or GANs Specializations, check your Coursera platform classroom for the invitation link if you haven’t joined the respective Slack workspaces.

To learn more about us,

[Deeplearningai]

2:28-9:56 keynote speech from Kian Katanforoosh, Founder & CEO, Workera
11:16- 47:50 Panel discussion
47:51- 59:50 Q&A

[cdtavijit]

My Notes:

1. Apply for the ML job if you can match the first three, or first few points of a job description. These are likely the most important ones. The whole list with diverse information is usually meant for inclusion purpose.

2. It is better to show that you are an AI + X (where X is your T shape expertise. E.g. I am a data scientist with expertise in NLP, "I am a data analyst with expertise in Marketing" as the companies are looking for someone who knows data engineering in general but also knows one domain well.

3. A key "soft skill" is a demonstration of technical communication where you can explain technical details to non-technical person as well as technical person. (My note: I am thinking experience of blog writing would be useful here).

4. If you are transferring from a different field, write that in the summary and showcase your ability using Competition like Kaggle, Github, example of side project so that the portfolio can serve as a qualifier. I remember I used to get this question a lot - how do we know that you can work in the industry.

[neuodev]

I really go great insights 💡 from this videos. Thanks every one participating in this video 🙌

[fgfanta]

The first 10 minutes and the the Q&A session at the end are the most relevant for ML, in my view; the rest is general advice that applies to intervies as s/w developer/engineer.

[abdulmukit]

Questions with timestamps and my notes:

5:50 - Chart explaining different ML roles out there and job responsibilities.
8:50 - Find your field of expertise and say you are AI + X expert.
13:16 - Match and mismatch between the applicant's profile and the job description.
=> First few points mentioned in the responsibilities section of the jobs post are most important. Your objective is to make it easier for the recruiter to see through your resume that you have those relevant skills or have done some work in those. You have to highlight those points in your objective statement. You can also mention relevant projects or papers that you did. Or even relevant side projects that you did. Present them clearly so that recruiters can quickly connect the dots.
17:36 - What does a recruiter sees in the resume in 7 seconds?
=> Make the most important asks of the job description abundantly clear in the resume.
18:13 - Large company or startup?
=> startups - impact, challenges, getting to experience a broad spectrum of roles in ML, quicker to climb the ladder. Slower growth, small impact. At bigger companies you may become out of touch with open-source tools and knowledge. That may make it harder for you to get the next job. At startups where ML is the core, it can be really rewarding in terms of career growth and impact. In large companies you may be assigned into a team that is not good for you.
** 24:40 - What are the Dos and don'ts during the interview process?
=> Speaker 1:
Prepare on technical interviews. Prepare to talk about your contributions and impacts in your past works. Ask questions.
Questions: How are technical decisions are made? How is the engineering culture?
Don'ts. If you don't know the answer then don't waste time. Accept that you don't know. But do reply how you would solve the problem if you faced now.
Do take feedback and listen to your recruiters. They do want someone who reacts to feedback positively.
=> Speaker 2:
Showing curiosity, creativity, knowledge of open-source or available tech. Showing potential is important. It is okay that you don't have all the perfect skills for the job.
=> Speaker 3:
Questions: What are differences between a ML Engineer and a applied ML Researcher at this company? Definitely ask these questions.
How are you tasked differently? How are you evaluated differently during performance review?

29:45 - Is PhD needed?
=> (to be continued.)

[dheerajagrawal]

Number of ML JOBS/Number of Software JOBS = 1/10

[tamasilles]

Any advice for fresh CS graduates with interest in ML? (No professional experience)

[RedShipsofSpainAgain]

50:34 Oh no! Don't call it "kegel" competitions, which sounds like a contest for pelvic floor exercises ;-) It's pronounced "kag-gull". First part rhymes with "bag" or "sag".