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Chip Huyen is the co-founder of Claypot AI, a platform for real-time machine learning (ML), as well as the author of best-selling computer science books like Designing Machine Learning Systems, which was published last May, and helpful eBooks like as Introduction to machine learning interviews. She is an Adjunct Professor at Stanford University and has previously worked at Snorkel AI and Nvidia.
But Huyen is also part of the committee that runs MLops Learners, a community of over 12,000 dedicated to learning and sharing best practices for ML production (MLops) and also hosting virtual and in-person events.
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There, Huyen helps out with the group’s Discord community where, he said, there’s currently a lot of discussion about job hunting, which isn’t surprising given recent tech layoffs, at both hot startups and Big Tech, which have including even the most highly skilled and sought after artificial intelligence (AI) and ML talent.
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AI and ML job seekers are on the rise
“I think it’s a bit scary for a lot of people,” he said. “I think one of the most popular channels on our Discord right now is under professional advice.”
Posts on Discord are anonymous, he added, allowing participants to share fears and anxieties in private. “We just hope that we can provide a means for people to express themselves and maybe other people can participate.”
She noted that even if someone hasn’t been fired but their coworkers have, there is a sense of “Am I next?”
“It’s a very natural instinct to start looking,” he said. “So we see a change in the market from a hiring perspective.”
But, he added, there is often uncertainty about which role to pursue at the moment, while the market itself drives people to take less risk.
“Someone recently said that they got an offer from their hometown and another offer from the UK,” he said. “Two years ago, they would be very excited to go to a new country and start. But now, he said, if I go to a new country and get fired, I’m stuck in the country. So I see a trend for people to be more hesitant to take risks, even for what could be really good jobs in big companies abroad.”
What AI and ML job seekers can do right now
Huyen emphasized that there are several things job seekers looking for their next AI or ML act can do to land the right role. While there may be differences depending on the type of company or industry a candidate is applying to, he said that overall, it’s about becoming more robust and agile in the face of change.
1. Differentiate yourself.
First, Huyen said, think about how you can differentiate yourself from other candidates for AI and ML jobs. “I see a lot of resumes, a lot of them are just identical,” she said. “[One candidate] he actually told us, I’ve put 4500 hours into python, it’s like, how do you measure that? But metrics don’t mean anything out of context.”
While it’s true that automated resume evaluations often require some of these types of metrics, for startups like Claypot AI, standard resumes aren’t enough. She said: “We encourage candidates to be creative with a side project, because we see a lot of value in coming up with interesting ideas and showing creative thinking.”
2. Focus on transferable skills.
Non-transferable AI and ML skills are very specific, Huyen explained, such as knowing the in-depth details of a specific framework or tool. These may not be transferable to other companies, for example a programming language like COBOL, but it is now outdated. “I want to look at more transferable skills because the scope of our work changes over time,” Huyen said. “So we want someone who just doesn’t know one thing, but has the skill set that will allow them to learn anything, like design thinking, knowing how to ask the right questions, knowing how to communicate ideas clearly. or be able to figure out what’s wrong. So if you find a problem, you don’t get stuck.”
3. Collect the best data engineering practices.
In a recent LinkedIn post, Huyen praised the rise of data engineer roles. “More and more data scientists are embracing engineering best practices (either by choice or necessity) and moving into data engineering. Data engineer roles might even be in higher demand than data science roles!
He noted that these are a perfect example of transferable skills. “I am always wrong to improve through engineering,” she said. “Machine learning is more specific, but if you have good engineering fundamentals, like system thinking, you can pick up on anything.”
4. Consider a generative AI side project.
“I think generative AI is a very exciting field and I think there are a lot of opportunities to build products beyond those. [tools]Huyan said. “So if someone is looking for a project, I highly recommend it – it’s where you can show a lot of creativity and not just sit at the keyboard and do what you’re told.”
It’s also an area with a lot of potential, he added: “When a field is saturated, it’s very easy to get discouraged because it can seem like whatever you do, someone else has already done it. But this, in my opinion, is still an open field.”
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Source: news.google.com