8 Paths to Getting a Machine Learning Job Interview
While you may be focused on your performance during your next job interview, landing that interview can be just as hard. Check out these tips for finding and securing an interview for a machine learning job.
By Jaxson Khan, CEO at Khan & Associates.
Scoring an interview for a machine learning job is sometimes the hardest (and most frustrating) part of getting the job, even more than getting through the interview. So in this article we're going to help you figure out how to land an interview in the first place for machine learning. This article comes from Jaxson Khan’s Ultimate Guide to Machine Learning Interviews.
I separated the section into both traditional approaches as well as newer proactive approaches, which could help separate you from the candidate pool at startups in particular.
Traditional Paths to Getting an Interview
1. Job Boards and Standard Applications
Most companies post jobs on their career site. You can always target a company and respond to a specific job posting or submit a general application expressing broader interest in the company through that job portal. You can also find machine learning job postings on websites like Indeed or LinkedIn. These are old faithfuls. Definitely invest some time there.
Recent job seekers have told me that they like Breakoutlist, AngelList, and Triplebyte.
There are specific job boards for the machine learning space, such as ML Jobs List, as well.
I've also heard that aggregators from VCs such as First Round Capital, Greylock, and Costanoa are quite good.
You’ll typically work with a recruiter during the interview process, but you don’t just have to wait for them to contact you. Sometimes you may want to reach out directly to recruiters, either at the company itself (the in-house recruiter) or third-party recruiters who put companies in touch with great candidates.
Some recruiters specialize in machine learning and artificial intelligence. Very often they include this in their LinkedIn profile title, or they may list something more general, such as "technology recruiter." What's important about recruiters is that they may know about the jobs that aren't even posted online. Keep in mind that something like 50% of the jobs are not even listed publicly. Quickly searching LinkedIn will give you a sense of some recruiters near you who might be able to find you the most relevant companies.
3. Job Fairs and Trade Shows
Job fairs present a rather daunting perspective: who wants to be milling around with a whole bunch of other candidates trying to chase down company representatives at a booth? But major universities, in particular, can have decent job fairs. However, my real recommendation is that networking events and meetups within your local machine learning community will probably be better than a traditional job fair. (Read on for more on those!)
Proactive Paths to Getting an Interview
While the options above are pretty traditional, it's more and more common for candidates to take a different tack when it comes to getting a job interview. Often you'll need to hustle and demonstrate creativity and grit in order to get a position. Startups are one of the main areas of new jobs in AI and machine learning and are known for pioneering a different type of interview style.
4. Attend or Organize an Event
This is often the best way to meet people interested in AI and machine learning in your community, and you may also learn about job opportunities from attendees. There are large conferences and smaller or more focused community meetups that you can target, depending on what you’re looking for.
International Conference on Machine Learning (ICML)
ICML is one of the leading international machine learning conferences in the world, with over 35 years in the business. Usually hosted in California, it's full of expert speakers on the current state and future of machine learning. You should consider this event if you're a machine learning engineer, but there's likely something for everyone connected to machine learning at this one.
Artificial Intelligence Conference
This conference focuses on the latest breakthroughs in AI and machine learning. It's an O'Reilly event that brings together science and business, featuring speakers from top software companies, training courses, and networking opportunities. It's a great place to learn about different applications of artificial intelligence. It's especially well-aligned to product managers and business intelligence developers.
Neural Information Processing Systems Conference
Another long-time conference (since 1987), this one is probably the most focussed on theory and research into the latest developments in machine learning. There is a significant focus on computational neuroscience, so it would be perfect for machine learning researchers or individuals from theoretical backgrounds to consider.
Sometimes it may be more in your interest to attend smaller community events where you're able to make a bigger impression and potentially even take on a leadership role after some time. It's also a way to get to know people in a local community. Often, large events are chock-full of vendors and people focused on bigger partnerships. Small events can give you easier access to hiring managers and help you connect with peers who could help you down the road. You also may be able to find an opportunity to snag a speaking gig or start to build your name within the community.
One of the best stops for meetups is very simply Meetup.com. You’ll find all sorts of them on the site. Some are quite large; for example, the NYC Machine Learning Meetup has more than 13,000 members. But don’t let that intimidate you; many people will register for a Meetup but not actually attend. Typically a smaller meetup is between 10-200 people.
Another alternative if you can't find a good meetup or one nearby: create one yourself! I know many candidates for roles that have gotten jobs because they started the relevant community group. It makes you a connector or an influencer in the community. And that's a great role to add to your CV.
5. Freelance and Build a Portfolio
There's no reason that you can't start doing machine learning work right away. One of the easiest ways is to start freelancing. This will likely be easiest for designers, engineers, and data scientists, though there certainly can be opportunities for researchers and product managers as well. Sites like Upwork or TopTal make it easy as a skilled professional to create a profile and find work in short-term contracts and ongoing projects, or even longer-term engagements.
A portfolio can help you build your brand and also be an online record of experience for your work. It can also give you some early references and testimonials that you can pass on to potential employers. And of course, it could allow you to do some genuinely interesting work that could inspire articles for blogs or other content that you can use to expand your profile.
Ultimately, freelancing may also simply be a good idea for you to validate the different types of work and industries that you are interested in, to help you narrow your job search and be more specific in the future.
Portfolios are focused on and given mentoring support through Springboard’s online machine learning boot camp if you’re looking for an extra boost.
6. Get Involved in Open Source
Another way to make connections in the machine learning community is to get involved in open-source projects. These are non-proprietary code bases and repositories that are worked on by distributed communities and are typically not meant for profit or owned by a company. They are often in open-source repositories on Github. This includes the Natural Language Toolkit project, which helps deal with human language as a data source, and the various libraries that make up the Python data science and machine learning toolkit.
Companies hiring engineers are often particularly known to hire based on open-source contributions, and sometimes will find you through what you wrote. It's similar to the portfolio effect. People will often look you up online and want to see what you worked on.
7. Participate in Competitions / Hackathons
If you prefer to use your skillset in a more confined or time-limited environment, perhaps a competition or a hackathon is the way to go.
There are machine learning competitions like Kaggle and many hackathons that allow you to work quickly on real business or social problems. It's a great way to put your skills in machine learning and artificial intelligence to use, and you will be able to meet people as well as showcase your ability to make a difference.
8. Informational Interviews
The final path in, or step that you can take, is a classic, but it may be irreplaceable. Ultimately, relationships are what can start you on the path to a job, and help you close the deal. More than half of jobs aren't even posted on job boards, and sometimes the only way through a company’s seemingly impenetrable shell is to start meeting people from that company and build strong relationships with them.
One of the best ways to network is to request only a little bit of someone’s time. A quick coffee date is ideal. Meet them on their schedule, at a place of their choosing. Reach out via email or a LinkedIn message with a very short note. All you need is one sentence about why you're unique as a candidate. You could also use this great framework from Steve Blank.
If you’re successful in getting coffee, treat it as an opportunity to seek advice and information from people in the field. And if you're good at growing your network, you’ll really get a sense of how the industry works.
Bio: Jaxson Khan is a CEO at Khan & Associates | Consultant and Writer for AI & Fintech | Volunteer for Education & Economic Development.
- What’s the Machine Learning Engineering Job Like
- How to Recognize a Good Data Scientist Job From a Bad One
- The Data Science Gold Rush: Top Jobs in Data Science and How to Secure Them