Machine Learning Resume : Sample and Writing guide

Creating your Machine Learning Resume

How to write a Machine Learning resume step by step

  1. Follow the right format
  2. Clearly classify Education Section
  3. Add relevant Skills clearly
  4. List relevant experience in Machine Learning  
  5. Add other sections to standout


  • Fonts – Professional resume fonts such as Calibri and Didot
  • Font size – Use 13-14 for headings and 11-12 for other texts
  • Format – The Reverse chronological format is the most recommended
  • Line Spacing – 1.1.5 (recommended)
  • File Type – PDF’s are the most preferred file type

Pro Tips

  • Use clutter-free design and do not fill every part of your resume with text
  • Using bullet points wherever possible is a better practice as compared to paragraphs
  • Always use active voice
  • Use simple vocabulary and shorter sentences
  • Do not try to fit in everything on one page. Use more pages if you feel the need for it but keep the number of pages as limited as possible.
  • Edit until you get a draft which is concise, clear in understanding, looks good visually, and includes all that you want to tell the recruiter about you
  • Use online tools such as Grammarly to self-check your draft
  • Have it proof-read by a third party, preferably a friend or a daily member who would give you genuine advice


  • A Bachelor’s degree in either computer science or in a related field
  • A good amount of prior experience with GPU computing and data mining
  • A general background in NLP and deep learning, along with their corresponding tools and techniques
  • Basic experience with agile software development practices

Character traits

  • Analytical and critical thinkers 
  • Data-driven performers
  • Clear communicators to translate and understand complex information
  • Problem solvers and innovator

Machine Learning Engineer Skills

Hard Skills

  • Java
  • PySpark.ML
  • Pandas
  • Data Modeling
  • Predictive Modeling
  • Regression
  • Classification
  • Clustering Models
  • Tensorflow
  • Pytorch
  • Keras
  • Numpy
  • Pandas
  • SciKit Learn
  • Unit Testing and CI/CD
  • Machine Learning technology
  • Explanatory Analysis
  • Natural Language Processing
  • C++(STL)
  • Python
  • Strong Programming Skills 
  • Data Structures

Soft Skills

  • Time Management
  • Critical Thinking
  • Organizational Skills
  • Interpersonal Skills
  • Presentation Skills
  • Teamwork and Collaboration
  • Written and Verbal Communication
  • Problem Solving
  • Attention to Detail

Pro Skills

Probability and Statistics

The theories of probability are the mainstays of the most machine learning algorithm. Being familiar with probability enables you to deal with the uncertainty of data. Getting a grasp of the probability theories like Python, Gaussian Mixture Models, and Hidden Markov Models; is a must if you want to be considered for a machine learning job that centres around model building and evaluation.

Closely linked to probability is statistics. It provides the measures, distribution and analysis methods required for building and validating models. It also provides the tools and techniques for the creation of models and hypothesis testing.
Together, they make the framework of the ML model building. This is the first thing to consider when building your machine learning resume.

Computer Science and Data Structures

Machine learning works with huge data sets, so fundamental knowledge of computer science and the underlying architecture is compulsory. Expertise in working with big data analytics, and complex data structures, are a must. Thus, a degree or a formal course in these domains is required for a machine learning career. Your resume must display your skills at working with parallel/distributed architecture, data structure like trees and graphs, and complex computations. These are required to apply or implement, at the time of programming. Additional certifications for practising problems and coding will hone your ability with big data and distributed computing. Experience in computer science applications will go a long way in securing you a job in this field.

Read Also: Linear Regression for Beginners – Machine Learning

Programming Languages – R, Python, Java

To apply for a job in Machine learning, you are required to learn some of the commonly used programming languages. It implements any language with the essential components and features, even though it is largely bound by concept and theory. Some programming languages are considered especially suited to complex machine learning projects. So, working knowledge of these programming languages adds value to your machine learning resume.

Using C/C++ when memory and speed are critical, helps to speed up the code. Many ML libraries are also developed in C/C++ as they are suited for embedded systems. Java, R & Python work very well with statistics. Python has several machine learning-specific libraries that make use of efficient processing, despite being a general programming language. Knowledge of Python helps train algorithms in various computing architecture. R is an easy-to-learn statistical platform, it’s use in ML and data mining tasks is increasing.

machine learning resume


If you are a fresher or an Entry – Level professional, give detailed information about the projects that you have done.

machine learning resume

Pro Tip: Do not shy away from giving all possible details about work experience and achievements, flaunt what you have achieved.  

machine learning resume

An application for machine learning job role requires careful planning and consideration. Machine learning is all about algorithms, which in turn stems from a good knowledge of big data analytics and requisite programming languages. Sound engineering or technical background is a must. By including these skills in your machine learning resume, you are increasing your chances of being selected. So, are you all set for a career in machine learning?

Take up the Basics of Machine Learning Free Online Course by Great Learning Academy and learn the basic concepts required for you to kick-start your machine learning journey. Great Learning offers various Artificial Intelligence Courses that you can choose from. Upskill with the help of mentored learning and dedicated career support.

Other Sections

degree, certificate or online diploma in these languages, ensures a good resume. As an engineer or student of science, you may already be skilled in C++, Java, and Python. You can also learn these languages online in your spare time, and practice on projects for special mentions on your CV. Programming languages like Python and R make it easy to work with data and models. Therefore, it is reasonable to expect a data scientist or machine learning engineer to attain a high level of programming proficiency and understand the basics of system design.
Read Also: 100 Most Common Machine Learning Interview Questions

Machine Learning Algorithms: Applying machine learning libraries and algorithms is part of any ML job. If you have mastered the languages, then you will be able to implement the inbuilt libraries created by other developers for open use. For instance, TensorFlow, CNTK or Apache Spark’s MLib, are good places to work upon. You can also begin with practising programming algorithms on Kaggle. You can mention this in your ML resume as well. 

Software Engineering and Design: Software Engineering and System Design, are typical requirements for an ML job. A good system design works seamlessly, allowing your algorithms to scale up with increasing data. Software engineering practices are a necessary skill on your resume. As an ML engineer, you create algorithms and software components that interface well with APIs. So technical expertise in software designing is a must while applying for a machine learning job.

Machine Learning Fresher Resume

When it comes to freshers, of course, they have no experience to showcase. Here, you focus more on your projects, certifications, internships, technical skillset, and soft skills.

The important skills to showcase on a resume are:

  1. Programming skills
  2. Statistics
  3. Data modelling and evaluation
  4. Machine Learning algorithms and libraries that you have worked with

The soft skills are the ones that make you an ideal employee and help the company function better. You can mention select accomplishments that showcase these skills, such as:

  1. A time you were a valued team member
  2. A time where you lead a team
  3. The specific problem you identified and solved
  4. When you followed directions
  5. A scenario where you stepped up beyond your responsibility

Explicitly explain the following points in your resume:

  1. Machine Learning Projects with objective, approach and results.
  2. Knowledge of any programming language
  3. Proven expertise in solving logical problems using data
  4. Training or internship in data analytics or data mining
  5. Highlight if you know Python or R

Your resume should be structured like this:

  1. Resume heading
  2. Personal and contact details
  3. Career objective
  4. Education
  5. Projects
  6. Certifications
  7. Internships
  8. Technical Skills and Soft Skills

Pro Tip: Always edit and restructure your resume on the basis of the job you are applying for. Accommodate job requirements in your skillset, achievement sections.

General tips to keep in mind :

  • There is no need to downplay your achievements and success. If there’s a place to boldly talk about your accomplishments, it’s on your resume.  
  • There is no need to fill every inch of your resume with text. White spaces provide a cleaner look to the document, making it much easier for the reader to comprehend. A good idea will be to adapt existing templates online, that equate well to your preferences.
  • Ensure that the writing is concise and to the point; eliminate any extra verbiage, unless necessary.
  • Do not confine your resume to a single page, there is no one-page mandate. As long as there exists relevant experience, the extra room is justified. 
  • Have it proofread, either online (on tools like Grammarly) or by a family member. This is useful to spot unseen errors and provide an outside perspective.

Further Reading

  1. Machine Learning Interview Questions and Answer for 2020 You Must Prepare
  2. 100+ Data Science Interview Questions for 2020
  3. Python Interview Questions and Answers for 2020
  4. NLP Interview Questions and Answers most commonly asked in 2020
  5. Python Developer Resume Samples| How to Make Python Resume?
  6. Top 20 Artificial Intelligence Interview Questions for 2020 | AI Interview Questions

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