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Engineers that specialise in machine learning work in the artificial intelligence sector. They work together to design programmes that let machines operate without being directly assisted by humans, with the help of data scientists, computer engineers and developers. Jobs that can use machine learning CVs include data engineers, data scientists and ML engineers.
Landing a machine learning job requires technical skills and expertise, and you’ll need to showcase this in a professional CV to help you get your dream position. Here are some aspects you should highlight for the recruiter in a machine learning CV:
Feature your contact information in your header. Include your full name, email address, address, phone number, and relevant social media like your LinkedIn profile or other professional portfolios. You should include this information in the header so that it is easy for the hiring manager to contact you if necessary.
Your professional summary or objective is a brief paragraph that goes at the top of your CV.
A professional summary is a better choice for those with plenty of experience since it is a snapshot of your career highlights thus far. It summarises your strengths as a candidate in two or three sentences:
Dedicated machine learning engineer with over five years of experience in designing and developing machine learning algorithms. Experienced in implementing analytics for different kinds of datasets. Versatile team player with a proven track record of finding innovative solutions.
The career objective, on the other hand, is a better option for those without much experience since it is aspirational. You use it to define your career goals and objectives.
Your skills section needs to show you have the broad technical skill set required to be a competent machine learning engineer. Consider listing the following technical hard skills:
However, don’t keep the focus solely on machine learning skills. Companies always want to see that you have the intangible abilities (also known as soft skills) to excel in the role, too. All companies value transferable skills like teamwork, communication skills, problem-solving and project management.
In your work history section, you’ll list recent relevant roles in reverse chronological order, along with a few major responsibilities and accomplishments for each role. Include any work on machine learning projects or computer science-related positions to establish your credibility for the recruiter.
Try to list only professional experience that is relevant to the advertised role. Search the job description for guidance on particular duties and achievements that match up with your previous roles.
In your education section, further establish your credibility by listing any major academic qualifications you may have. There aren’t many courses available that are entirely focused on machine learning because it’s a relatively young field. As a result, an undergraduate bachelor’s degree in a related discipline, such as computer science, statistics, electrical engineering, mathematics or the physical sciences, is frequently accepted, along with any more specialised coursework.
Having awards in machine learning is a great asset to add to your CV and will really make you stand out against other candidates. Whether these achievements are regarding AI or a specific industry experience, it is good to include them in your job application!
If you don’t have relevant education in machine learning, there are multiple certifications that you can make a note of instead. For example, completion of online courses in areas such as software development and programming skills can bolster your CV.
There are a multitude of CV examples and templates available for you to use that can fit into your machine learning role. Simply follow our example above to ensure you have filled in all the relevant information that machine learning recruiters will be looking for!
Now that we’ve gone through the structure of a machine learning CV, here are some extra writing tips that will help you when crafting your own CV.
Use industry-specific jargon and acronyms to showcase your expertise. References to the knowledge of SQL, Spark, Hadoop, TensorFlow and NumPy may not mean anything to most people, but they will indicate to the recruiter that you know your stuff. In addition, you can use the job description to customise your CV to the advertised role, hitting specific keywords that the recruiter is looking for.
Last, don’t write a machine learning CV without help! Use our CV builder for job-specific writing tips on how to create the perfect CV with CV templates.
You always need to write a cover letter for a professional job application. For a machine learning role, your cover letter lets you expand on some of your technical skills and identify your specialities. It also gives you the opportunity to explain your career goals and why you’d be an excellent fit for the company.
It is always a challenge to land a role without much previous experience. In this case, you’ll need to highlight your skills and qualifications to demonstrate that you can perform the role’s duties. Add a strong cover letter that explains to recruiters how you can contribute, and why they should take a chance on you based on the abilities you already have.
Use the job description included in the job ad for each role that you apply for. You should then tailor your CV so that it directly addresses each position’s needs, even using specific keywords (e.g., particular skills or requirements) mentioned in the posting. Check out our machine learning CV samples for inspiration.
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