Machine learning engineering is part of artificial intelligence’s sub-branches. It consists of developing high-performing predictive systems and applications through data modeling and algorithms. These programs work with large volumes of data and learn from it to deliver more accurate predictions.
Machine learning engineers are responsible for delivering these applications and systems. They must also perform machine learning tests to measure the efficacy of statistical analysis.
Machine learning is a highly in-demand field used for different purposes, including credit scoring, facial recognition, and brain tumor detection. Almost every industry can benefit from the magic behind machine learning engineering.
Hence, it’s no surprise to see that the demand for machine learning engineers is expected to rise by up to 22 percent, according to the Bureau of Labor Statistics. This is considerably high compared to the average demand outlook for most occupations. Yet, this is no excuse to neglect the quality of your resume.
Despite the high demand for machine learning engineers, you should still give your potential employer reasons to hire you. Today, we’ll explore some industry-relevant techniques to improve the quality of your resume and increase your odds of getting a job as a machine learning engineer. Don’t forget to check our four resume templates.
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A machine learning engineer creates autonomous systems that use large volumes of data to deliver accurate predictions. These applications learn from experience and need minimal to zero human intervention. Additionally, you should select the appropriate data visualization techniques and tools. This is a skill you might want to include in your resume.
Cloud technology has become more popular over the last decade due to its security and accessibility. This makes it essential for machine learning engineers to use them, so most employers might expect you to be able to use tools like AWS or Microsoft Azure. If you are familiar with cloud technology, indicate it in your resume.
A great way to set you apart from your peers is by including relevant certifications on your resume. This way, you are showing evidence of your skills and level of experience by a validated source. Some of the best certifications you can use are AWS Certified Machine Learning and Google Cloud’s Professional ML Engineer Certification.
A machine learning intern assists the development team by writing code to develop autonomous systems. You should ideally have mathematical skills, basic programming and data science knowledge, and a passion for AI. If you are struggling to include something in your work history section, you can also include volunteering experience on your resume.
Your skills section should be highly technical, so it’s important to fill it in with hard skills like the programming languages and tools in your toolkit. This will give recruiters a better idea of the type of ML systems you can build. Some of the most popular coding languages in ML are Python, Java, and C++.
It’s ok if you don’t have extensive experience in the field. In the end, an internship experience will give you the expertise you need. You can include independent ML projects on your resume to help you demonstrate your potential.
A senior machine learning engineer is an experienced professional who develops machine learning models, conducts research, and supervises less experienced colleagues. It is your responsibility to ensure that ML projects run smoothly. This is a role that requires seniority, so it’s important to highlight your education and relevant work experience on your resume.
Deep learning consists of neural networks with multiple layers. It is an ML technique that resembles the human brain’s functionality. Hence, you can work with large volumes of data to create high-performing systems. As a senior machine learning engineer, you should be familiar with this technique.
Senior machine learning engineers often supervised other colleagues with less experience. They are also part of onboarding and occasionally participate in the hiring process. You should demonstrate this in your resume.
Natural Language Processing is the process of developing devices and applications that understand human speech and sounds. These programs analyze the structure of speech to determine the meaning of words. Today, this is a highly in-demand field of artificial intelligence, and we use it unnoticeably in our daily life, such as voice recognition keyboards and remote controls. You should indicate your syntactic and semantic parsing skills on your resume.
Eventually, all that data you collect and process has a purpose: helping organizations make more informed decisions and improve their services. That’s why it is important to have statistical analysis skills and showcase them on your resume.
As an Artificial Intelligence (AI) Specialist, your role is as fascinating as it is complex. You live in a world where you're programming machines to mimic human intelligence. That's no small feat. Companies are increasingly relying on AI to solve problems, make predictions and drive improvements, an evolution that's ramped up the demand for AI specialists. Given this trend, your resume needs to show that you're not just familiar with AI, but you're a master of it. Furthermore, as AI continues to evolve and get more sophisticated, companies are searching for candidates who can keep up with shifts in the field. So, when crafting your resume, don't just focus on what you've done, but how you're continually learning and staying ahead of the game.
As an AI Specialist, you're expected to be proficient in languages like Python, R, and Java, and be comfortable working with AI-related tools like TensorFlow or Keras. Hence, don't just list these, but provide examples of how you've applied them to build or enhance AI systems.
An understanding of trends influencing the development and application of AI is crucial. So, describe any ongoing learning or certifications you're pursuing. Additionally, articulate your understanding and consideration of AI ethics. Include instances where you've had to consider the ethical implications of AI systems you've worked on.
We spoke with hiring managers and recruiters at top companies like Google, Amazon, and Microsoft to gather their best tips for creating a strong machine learning resume. They emphasized the importance of showcasing your technical skills, relevant projects, and business impact. Use the following tips to make your machine learning resume stand out from the competition and land your dream job.
Hiring managers want to quickly see if you have the right technical skills for the job. Make sure to list your proficiency in key machine learning skills, such as:
Don't just list the skills though. Provide concrete examples of how you used them to drive results:
A resume summary is an optional section that highlights your most impressive and relevant skills and experiences. While not mandatory, it can be beneficial for machine learning professionals to provide context and showcase their unique value proposition. Avoid using an objective statement, as it focuses on your goals rather than how you can contribute to the employer.
When crafting your summary, tailor it to the specific machine learning role you're targeting. Emphasize your technical expertise, key projects, and measurable achievements. Be concise and objective, using metrics to quantify your impact whenever possible.
The work experience section is the heart of your machine learning resume. It's where you highlight your most relevant and impressive work, projects and achievements. Aim to tailor this section to the job description and focus on demonstrating the impact you've had in previous roles.
The education section of your machine learning resume should be concise yet impactful. It's an opportunity to showcase your relevant academic background and specialized training that prepared you for a career in this field. Let's break down the key elements to include and how to present them effectively.
Even though this is a highly technical role, you should still demonstrate excellent communication skills. Your resume should be direct and precise. That’s why you should take care of your word economy since you have limited space. This is where action verbs can help you; they provide an accurate description of your duties or accomplishments in just a single word.
You should ideally use different vocabulary on your resume that is highly related to your profession. That’s why we have selected a list of action verbs for your machine-learning resume. This will help you improve your readability and seamlessly describe your professional experience.
For a full list of effective resume action verbs, visit Resume Action Verbs.
A machine learning engineer should have a combination of data science and software engineering skills. This is what you should demonstrate in your resume. Mention the programming languages you are familiar with, such as SQL, Python, R, and C. Additionally, you should include ML fundamental skills such as deep learning, data structures, and neural networks.
Your machine learning resume should focus on technical skills. You can demonstrate your soft skills throughout your resume, but it’s better to maintain the skills section focused on technical competencies. If you’d like to know more ideas to include in your resume, take a look at our selection of hard skills for machine learning engineers.
You can include the above skills in a dedicated Skills section on your resume, or weave them in your experience. Here's how you might create your dedicated skills section:
This word cloud highlights the important keywords that appear on Machine Learning job descriptions and resumes. The bigger the word, the more frequently it appears on job postings, and the more 'important' it is.
Thank you for the checklist! I realized I was making so many mistakes on my resume that I've now fixed. I'm much more confident in my resume now.
