IN THIS ARTICLE
- Why Is a Machine Learning Resume So Important When Looking for a Job?
- Machine Learning Resume Components
- How To Build a Strong Machine Learning Resume
- Machine Learning Resume Tips for Different Career Stages
- Machine Learning Resume Samples
- Machine Learning Resume FAQs
Get expert insights straight to your inbox.
Artificial intelligence is transforming industries from manufacturing to healthcare, and demand for AI practitioners has increased commensurately. Machine learning jobs are projected to be worth nearly $31 billion by 2024. According to LinkedIn’s 2021 Jobs on the Rise report, strong demand for artificial intelligence practitioners is a top emerging job trend.
Machine learning engineers command an average base salary of $135,202 annually, and common benefits include insurance, stock options, and unlimited PTO. To land a machine learning engineering role, you’ll need robust technical knowledge, diverse applied skills, and a well-crafted resume that communicates your expertise to recruiters and hiring managers.
Why Is a Machine Learning Resume So Important When Looking for a Job?
Whether you are an AI Engineer or Data Scientist your machine learning resume introduces you to potential employers, and an effective resume is a baseline requirement for moving forward in any hiring process. Hiring managers will use your resume to swiftly assess whether you are eligible and qualified for a particular role. A properly structured resume will grab the attention of potential employers.
Your resume is also an opportunity to demonstrate how your skills and experience align with the unique responsibilities of the position in question. An eye-catching resume that succinctly conveys the value you will add to an organization could land you an interview—as long as you present the relevant information correctly.
Machine Learning Resume Components
Your resume is an opportunity to celebrate your accomplishments—just keep your wording concise. White space improves readability and makes your resume easy for a hiring manager to scan in a pinch. When organizing your resume, be sure to break down key information into the following sections:
If you’re angling for an entry-level machine learning role, you might not have much work experience under your belt. The education section of your resume will help fill your gaps in experience while simultaneously validating your technical skills. Whether you have a bootcamp certificate or a Ph.D., use this section to briefly discuss relevant coursework and impressive academic achievements.
The skills you emphasize on your resume can determine whether your application advances in the hiring process. Consider tailoring your resume to the description of the role you’re applying for—employers want to see that your skills align with their needs. In the skills section, machine learning engineers should strive to convey proficiency with scripting languages, a robust understanding of machine learning concepts, and foundational knowledge of algorithms, statistics, and data processing.
This section summarizes the hands-on work you’ve done and will help decide whether you move forward in the hiring process. This is your opportunity to discuss professional work experience, internships, volunteer work, and more. If your prior experience lies outside the field of machine learning, be sure to relate what you’ve done to the requirements of the job you’re applying for. That could mean highlighting a mastery of desirable soft skills, industry knowledge, and more.
If applicable, emphasize prior experience with GPU computing, data mining, Apache Spark, and the agile software development process. You’ll also want to demonstrate knowledge of natural language processing (NLP), deep learning, and associated tools and techniques.
If you don’t have work experience, you can compensate with a robust compilation of projects. A strong projects section will validate your end-to-end technical skills, convey critical thinking abilities, and show your capacity to tackle real-world business problems. Strive to emphasize instances in which you built a working prototype and deployed that prototype to production. Also, be sure to build a narrative around each project that contextualizes the problem and communicates both your process and your motivations.
How To Build a Strong Machine Learning Resume
Pick a Template That Showcases Your Experience
Tailor Your Resume to the Job Posting
Showcase Your Successes
To create a machine learning resume that opens doors, you’ll need to:
Pick a Template That Showcases Your Experience
While stylish formatting is less critical for machine learning engineers than for UI/UX designers or creative directors, the template you choose for your resume matters. The most effective machine learning resumes are designed for readability and offer pleasant aesthetic touches that don’t distract from the content. Basic resume templates are available for download or are accessible through popular word processors.
The following information should be included in your machine learning engineering resume. The fields are ranked by importance, but you may reshuffle the order according to your experiences and what you wish to highlight:
- Header. In this field, state your name, address, and contact information.
- Personal summary. This field is a three to five sentence synopsis of who you are and what you’ve accomplished. Your resume summary is an opportunity to reel in the hiring manager with a compelling narrative. Show your personality, celebrate major successes, and outline why you’re the perfect fit for the company.
- Experience. This field should detail your relevant prior work history. Describe each experience with a brief summary of your responsibilities and achievements, and note the duration of your employment with dates. If you have less experience, include one or two internships that are pertinent to your desired position.
- Projects (optional). This field highlights and briefly summarizes applicable projects you’ve completed which the prospective employer may find of interest. When describing the projects, be sure to explain the problem, offer insight into your process, and describe your results.
- Education/certifications. In this section, note degrees you’ve earned, courses you’ve completed, and other pertinent professional certifications. If you’re an early-career professional, feel free to add your grade point average—as long as it’s higher than a 3.0.
- Skills (optional). In this field, you’ll be able to mention skills and technical expertise that you weren’t able to incorporate in other sections of your resume. Be sure to note your proficiency with scripting languages like Python and Java, as well as your knowledge of key machine learning libraries and frameworks like Scikit-learn, TensorFlow and more.
- References (optional). In this section, include the contact information of two or three people who are familiar with your skills and work ethic and are willing to speak on your behalf. These could be mentors, former instructors, or even previous clients. It’s vital to request permission from your references before listing them on your resume—if your references are caught off guard, it reflects poorly on you.
Your resume should be customized to showcase your unique strengths and experiences, so feel free to add or eliminate fields according to your credentials. For example, you can add a section dedicated to honors and awards you’ve won, or you could fold that information into your personal summary or experience sections. The way you describe your professional background should also convey your critical thinking and problem-solving skills.
Get To Know Other Data Science Students
Tailor Your Resume to the Job Posting
Resist the temptation to send a one-size-fits-all resume to every company. Instead, strive to customize your resume to the specifications of each job you apply for. If a job description requests that candidates possess specific programming knowledge, bring that skill to the forefront on your resume. If you’ve completed a certification that correlates with a requirement noted in the job posting, highlight that accomplishment. If an employer requests experience in a particular domain like computer vision, make note of computer vision projects you’ve completed in the past.
This applies to soft skills and industry knowledge as well. Use your resume to emphasize how your personality and past experience align with the company’s culture and values. If an employer is looking for a candidate who fits with their social mission, include a volunteer section on your resume to showcase your social consciousness.
By personalizing your resume, you’ll help employers connect the dots and envision you in the role you seek. You can make the hiring manager’s job easier by showing how your skills, knowledge, and experience apply to the position—and boost your chances of winning an interview. An effective resume will utilize verbiage from the original job posting and demonstrate how your contributions will benefit the company. Furthermore, if it’s obvious that your resume wasn’t customized for a particular role, a hiring manager may pass over your application.
Showcase Your Successes
Don’t let your resume morph into a long list enumerating every work-related responsibility you’ve ever shouldered in previous positions. Including too many details on your resume makes it difficult for hiring managers to find the vital information that will help advance your application in the hiring process.
Instead, focus on achievements and significant projects you’ve completed. Frame your successes in ways that demonstrate how you will add value to the company. Employers aren’t just interested in what you’ve done—they’re interested in what you can do for them. Don’t be afraid to brag, and be sure to concisely and explicitly describe how these experiences will translate to a value-add in your new potential role.
When describing prior accomplishments, emphasize outcomes. Demonstrate how your previous work made a positive impact. What results did your project yield? Did your work improve workplace processes? Use performance metrics and any other applicable quantifiers to convey the impact of your work. Note any awards or accolades that your projects may have earned—these will also serve to validate the quality of your work.
Machine Learning Resume Tips for Different Career Stages
As your career advances, you’ll need to adjust your resume to reflect new experiences and credentials. Here are a few tips to boost your resume at every career stage:
Entry-Level Machine Learning Resume
Typically, an entry-level machine learning engineer has 0-4 years of experience. Someone at this career stage might have recently graduated from college or switched careers and pivoted into the field. Entry-level applicants who lack professional experience should emphasize end-to-end machine learning projects that have been deployed in production—hiring managers want to see execution capability.
Mid-Level Machine Learning Resume
Mid-level machine learning engineers have 5-9 years of experience. At this career stage, your specialization has likely narrowed to a particular focus. To stay relevant, add new credentials you’ve accumulated to update your skills—examples might include credentials that certify your ability to implement machine learning solutions in particular cloud-based environments like Amazon Web Services.
Senior-Level Machine Learning Resume
Senior-level machine learning engineers have 10+ years of experience in the field and are typically hired by large, multinational corporations. Senior machine learning engineers often lead the design, deployment, and scaling of data and machine learning infrastructure, and may collaborate closely with other departments like dev ops, data science, and product. In addition to emphasizing end-to-end expertise in ML pipelines, senior machine learning engineer resumes should also highlight prior managerial experience, as senior positions often are responsible for leading teams or departments.
Machine Learning Resume Samples
Need some inspiration for your machine learning resume? Check out these machine learning resume examples compiled by professionals working in the field.
This machine learning resume by Nikita Kozodoi is easy to parse and makes outreach a breeze. Kozodoi has included live links to his Github, LinkedIn, and email address to facilitate contact and further exploration of his work. Finally, the date on the resume shows that all the information is current.
This resume by Eric Wadkins exemplifies how early-career machine learning engineers who haven’t held many machine learning roles can use internships and academic projects to build an effective experience section. The logos included with each experience also enhance brand recognition and drive home the impact of Wadkins’ prior work with influential organizations.
This resume by Jake Ralston features a concise, informative personal summary that offers insight into his areas of expertise. When summarizing his prior experiences, Ralston discusses the machine learning solutions he implemented in the context of real-world business problems. He also highlights the impact of his work and quantifies his successes whenever possible, demonstrating strong communication skills.
Machine Learning Resume FAQs
Wondering what skills and experience you need to break into machine learning? Here are some of your top questions, answered.
What Skills Are Required for Machine Learning?
Machine learning lies at the intersection of software engineering and data science. Machine learning engineers need a strong background in software engineering and computer science to write algorithms that can sort, search, and optimize data. Necessary data science skills include proficiency with programming languages like Python, Java, and SQL, as well as fluency in mathematics, probability, and statistics. You’ll also need to be able to conduct data analysis with big data frameworks like Spark and Hadoop. Finally, machine learning engineers need additional machine learning skills related to deep learning, natural language processing, algorithmic optimization, neural network architectures, and more.
Can You Get a Machine Learning Job With No Experience?
To land a machine learning job without professional experience, you’ll need to demonstrate a robust set of applied skills and showcase a strong portfolio of end-to-end machine learning projects. Participating in hackathons, contributing to open source projects, and designing, building, and deploying your own projects will be crucial to getting your foot in the door.
Do You Need a Degree To Get Into Machine Learning?
Above all, hiring managers want to see that candidates are able to deploy ML models in production. While a bachelor’s degree won’t hurt your chances of getting hired as a machine learning engineer, the most important asset you can bring to the table as an applicant is a robust, proven technical skillset. In lieu of a degree program, you can acquire these skills through a machine learning bootcamp program with a strong focus on hands-on, project-based learning. If you’re able to validate those skills with an outstanding project portfolio and well-structured resume, you’ll be well on your way to landing your first machine learning role.
Since you’re here…
Curious about a career in data science? Experiment with our free data science learning path, or join our Data Science Bootcamp, where you’ll get your tuition back if you don’t land a job after graduating. We’re confident because our courses work – check out our student success stories to get inspired.