4 AI and ML Job Search Tips from Chip Huyen

Chip Huyen is the co-founder of Claypot AI, a real-time machine learning (ML) platform, and the author of best-selling computer science books such as: Machine learning system designPublished last May, Machine Learning Overview Interview. She is an Adjunct Lecturer at Stanford University and previously worked at Snorkel AI and she at Nvidia.

But Huyen is also a member of the committee that runs MLops Learners. MLops Learners is his community of over 12,000 people dedicated to learning and sharing best practices in ML production (MLops), which also hosts virtual and in-person events.

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So Huyen helps out in the group’s Discord community, where there’s a lot of discussion about job hunting right now. Given the recent cuts in tech jobs, this isn’t surprising. Some of the most seasoned and sought-after artificial intelligence (AI) and ML talent at both high-profile startups and big tech companies.

AI and ML job seekers on the rise

“I think it’s a little scary for a lot of people,” she said. “Right now, I noticed that one of our most popular channels on her Discord is getting career advice from her.”

Discord posts are anonymous, so participants can share their fears and anxieties privately, she added. “I just hope that we can provide a space for people to express themselves, and maybe others will join us,” she said.

She noted that even if someone isn’t fired, even if a colleague is fired, there’s a sense of, “Will I be next?”

“It’s a very natural instinct to start looking,” she said. “So we are seeing a shift in the market in terms of adoption.”

But while the market itself reduces risk for people, there is a lot of uncertainty about what role it should pursue now, she added.

“Someone said recently that they had an offer from the local area and then an offer from the UK,” she said. “Two years ago they were so excited to go to a new country and start. So I see people tending to be hesitant to take risks even if they can land a really good job in a big company abroad.”

What AI and ML job hunters can do now

Huyen emphasized that job seekers looking for their next AI or ML job can do a few things to land the right position. She said there may be differences depending on the type of company or industry a candidate applies to, but overall it’s all about making yourself more robust and agile in the face of change.

1. Differentiate.

First of all, think about how you will differentiate yourself from other AI and ML job seekers, said Huyen. “I look at a lot of resumes and a lot of them are exactly the same,” she said. “[One candidate] In fact, he told us, we spent 4,500 hours on Python. How does it measure it? But without context, metrics mean nothing. ”

It’s true that automated resume screening often requires these kinds of metrics, but for startups like Claypot AI, a cookie-cutter resume isn’t enough. “We encourage candidates to be creative with side projects because we believe there is a lot of value in having interesting ideas and showing creative thinking,” she said. ‘ said.

2. Focus on applicable skills.

Huyen explained that non-transferable AI and ML skills are very specific, such as knowing the details of specific frameworks and tools. These may not be transferable to other companies (for example, programming languages ​​like COBOL), but are now obsolete. “Our scope of work changes over time, so we want to look for more applicable skills,” Huyen said. “So we don’t want people who know only one thing, but people who have a set of skills that can understand anything: design thinking, how to ask the right questions, how to articulate ideas. Or being able to understand what the problem is, so that when something goes wrong, you don’t just get stuck.”

3. Cover data engineering best practices.

In a recent LinkedIn post, Huyen praised the growing role of data engineers. “More and more data scientists are adopting engineering best practices (by choice or need) and moving into data engineering. Data engineer roles are likely to be in higher demand than data science roles.” There is even.”

These are good examples of applicable skills, she noted. “I always get it wrong trying to get better through engineering,” she said. “Machine learning is more concrete, but with a good engineering foundation, like systems thinking, you can learn anything.”

4. Consider the Generative AI side project.

“I think generative AI is a very exciting area, and I think there are a lot of opportunities to build products on top of them.” [tools]said Huen. “So if anyone is looking for a project, I highly recommend it. It’s a place where you can get a lot of creativity done, rather than just sitting at a keyboard and doing what you’re told.”

It’s also an area with a lot of potential, she added. “When the field becomes saturated, it’s very easy to get discouraged because everything you come up with feels like someone else has already done it. It’s still a wide open field.”