Machine Learning Engineer Resume Examples

A machine learning resume is a document used to apply for a machine learning job. A strong resume is essential for securing an interview for a Machine Learning Engineer position. Resumes should list your skills, experiences and qualifications. Through your resume, an employer should see why you are a great candidate for the role.

There are a few steps you should take before you start writing your machine learning resume, as well as several best practices to keep in mind while you write.

Before You Write

Research the company: Resumes and cover letters need to be tailored to the position you are applying for. To do this, you first need to learn more about the company. Take a look through their website, social media and news or press releases. By getting a sense of their work, values and objectives, you can customize your resume to address their specific needs.

Compose a master resume: Compile a list of all your work-related accomplishments in one document. Your master resume acts as a reference guide for you. When you begin drafting your resume, select the experiences that are most relevant to the job posting.

As You Write

Be concise: Your machine learning resume should be a one-page document. Hiring Managers may be reviewing hundreds of applications, so keep your resume short and focused.

Choose a clean template: The layout of your resume should be organized, readable and aesthetically pleasing. The design should not distract from the content.

Use bullet points and headings: Keep your resume organized with clear headings and bullet points. Remember to include ample white space—you don’t want your resume to be too cluttered.

Opt for action verbs: Choose impactful action verbs that highlight your achievements. Examples of action verbs include: solved, accelerated, deployed, reduced and conceptualized.

Emphasize your successes: Use accomplishment statements that follow the formula action verb + task + result. For example, “Developed model to predict stock prices with 98% accuracy, enabling company to make informed investments.”

Add numbers and key metrics: To show employers the impact of your work, include concrete numbers and stats wherever possible. Quantify your accomplishments so the company can clearly see the value you would bring.

Edit and review: Go through your resume and check for spelling, grammar or typographical mistakes. Keep your content to the point and eliminate any superfluous descriptions.

Ask for feedback: Send your resume to a trusted colleague, friend, family member or mentor. It can be helpful to have an outside perspective and a fresh pair of eyes.

A resume should show why you are the best candidate for a Machine Learning Engineer job. Your resume is an introduction and pitch to the employer—it is your way of convincing the company that you would be an asset. Your resume should be framed around how you can help the company achieve their goals. A strong resume will ultimately help you secure an interview.

A good outline to follow for your machine learning resume is:

1. Header: List your contact information (name, phone number, email) and include links to your portfolio and/or GitHub profile.
2. Profile/Summary: Emphasize key points from your resume that show why you are the best candidate for the role.
3. Experience: Outline your top machine learning successes.
4. Projects: Highlight relevant machine learning projects.
5. Education: Include degrees/certificates/training.
6. Skills: Include relevant technical skills that match the job description.
7. Extra Sections: Add in conferences, published papers, awards and other activities or interests.

Your Machine Learning Engineer resume should include a profile, work experience, machine learning projects, technical skills, training/education and extras.

Profile: A strong profile/summary highlights your best features. Your profile should convince the Hiring Manager to keep reading the rest of your resume. In two to four sentences, tell the employer who you are, describe your machine learning successes and explain what you would bring to the role.

Work experience: List your most relevant work experience in reverse chronological order, with your most recent experience first. Each experience should include your job title, the company and dates you were employed. In a few bullet points, describe your main accomplishments. Remember to focus on successes. For example, “Used logistic regression models to make predictions” doesn’t say much about your work. To reframe it as a success, you could write, “Applied logistic regression model to predict product sales to within 2%.”

Education: Include post-secondary degrees and any other certifications. You can also highlight relevant coursework, academic achievements or scholarships.

Projects: Machine learning projects are evidence of your skills—they show employers what you can achieve. Include the title, a link to the project and your role. Provide a brief description of the project, along with tools used.

Skills: List your technical skills that match what is listed in the job description. Extras: Include additional sections that will help you stand out. For example, you could add in conferences attended, papers published or awards received. These also help show your passion and dedication to your craft.

The skills you include on your Machine Learning Engineer resume should match the ones listed on the job description. Keep in mind that some companies use automated resume scanning software. To make sure you pass that initial screen, you will want to include key skills or phrases from the job description.

While skills will vary depending on the position, there are a few common skills that most Machine Learning Engineers should have. Here are a few skills that employers often look for in candidates:

• Data structures
• Data modeling
• Data visualization
• Predictive modeling
• Statistical modeling
• Regression
• Clustering and classification
• Web scraping
• Tensorflow
• Pytorch
• Keras
• Numpy
• Pandas
• SciKit Learn
• MATLAB
• Explanatory analysis
• Natural Language Processing
• PySpark.ML
• Jupyter Notebook

You will also be expected to know programming languages, such as:

• C++
• Python
• Java
• R
• Lisp
• Prolog

[NAME]
[Phone Number]
[Email]
[GitHub /portfolio link]

SUMMARY/OBJECTIVE

Skilled machine learning engineer with expertise in [area of expertise]. Successfully [major machine learning accomplishment or project]. Eager to bring experience in [top skills] to help [company].

EXPERIENCE

[Job title, Company]
[Month, Year – Month, Year]

· [Action word] [skill/task] [result/impact]
· [Action word] [skill/task] [result/impact]
· [Action word] [skill/task] [result/impact]

[Job title, Company]
[Month, Year – Month, Year]

· [Action word] [skill/task] [result/impact]
· [Action word] [skill/task] [result/impact]
· [Action word] [skill/task] [result/impact]

EDUCATION

[Degree earned, School name]
[Graduation date]

· [Relevant courses]
· [Academic achievements]

SKILLS

· [Technical skills]
· [Programming languages]

PROJECTS

[Project], [Role]
[Brief description of project]

ADDITIONAL ACTIVITIES

· [Conferences]
· [Volunteer experience]