Machine Learning Engineer Resume Example & Writing Guide

Use this Machine Learning Engineer resume example and guide to improve your career and write a powerful resume that will separate you from the competition.

If you have a knack for coding, an eye for detail, and a passion for problem solving, you might be ready to make the move into the world of machine learning. Machine learning engineers are tasked with building new algorithms that can learn from data and make predictions based on past experiences.

Machine learning is one of the fastest growing fields in computer science, and companies are looking for people with machine learning experience to help them solve complex problems and take their businesses to the next level. If you’re ready to take your career in this direction but aren’t sure where to start with your resume, here are some tips and an example for reference when writing yours.

Mary Thompson
Phoenix, AZ | (123) 456-7891 | [email protected]

Machine learning engineer with five years of experience in data mining, predictive modeling, and algorithm development. Expertise in Python and Scikit-learn. Passionate about developing software that can make a difference in people’s lives.

University of California, Berkeley Jun '10
M.S. in Electrical Engineering and Computer Science
University of California, Berkeley Jun '06
B.S. in Electrical Engineering and Computer Science
Company A, Machine Learning Engineer Jan '17 – Current
  • Developed a deep learning model to detect and classify anomalies in the manufacturing process of an industrial robot, reducing downtime by 50%.
  • Designed and implemented a machine learning algorithm for detecting abnormal behavior in robots using computer vision techniques (SIFT, HOG).
  • Implemented a neural network based anomaly detection system that reduced false positives by 80% compared to existing solutions.
  • Built a web application for monitoring the health of robots using React/Redux with NodeJS backend and Python scripts for data collection from sensors on robots.
  • Conducted research into new technologies such as TensorFlow LSTM networks for speech recognition and OpenCV object tracking algorithms for improving robotic performance metrics.
Company B, Machine Learning Engineer Jan '12 – Dec '16
  • Developed a machine learning algorithm to predict the probability of fraudulent transactions based on transaction data
  • Created an automated email classification system using Naive Bayes classifiers and Support Vector Machines (SVM)
  • Implemented a recommendation engine that used collaborative filtering techniques for e-commerce customers
  • Improved customer experience by identifying trends in support tickets through natural language processing tools
  • Conducted A/B testing on new features, landing pages, emails and sales copy before deploying them live
Company C, Data Analyst Jan '09 – Dec '11
  • Conducted data analysis to support marketing initiatives and business decisions.
  • Identified trends and insights in data sets to inform business decisions and marketing strategies.
  • Created and maintained marketing reports to track progress against marketing goals.
  • Machine Learning Engineer Certification
  • Kubernetes Certification
  • Apache Spark Certification

Industry Knowledge: Machine Learning, Artificial Intelligence, Neural Networks, Statistical Analysis
Technical Skills: Python, TensorFlow, Scikit-Learn, R, Numpy, Pandas, SciPy, H2O, Microsoft Office Suite
Soft Skills: Communication, Teamwork, Problem Solving, Critical Thinking, Decision Making, Leadership

How to Write a Machine Learning Engineer Resume

Here’s how to write a machine learning engineer resume of your own.

Write Compelling Bullet Points

When you’re writing your bullet points, it can be tempting to focus on the technical aspects of your work. But that’s not the most effective way to showcase your skills.

Instead, you should focus on the outcomes of your work—the results you achieved and the value you provided to your company. So rather than saying you “developed machine learning algorithms,” you could say you “developed machine learning algorithms to automate customer service requests, reducing average response time from 2 hours to 15 minutes.”

The second bullet point paints a much clearer picture of what you did and the impact of your work. And it provides a specific number to demonstrate how much time and money you saved your company.

Identify and Include Relevant Keywords

When you apply for a job as a machine learning engineer, your resume will likely be scanned by an applicant tracking system (ATS) for certain keywords. This program looks for specific terms related to the position, like “big data” or “machine learning” in order to determine whether your skills are a match for the job. If your resume doesn’t include enough of the right keywords, your application might not even make it to a human recruiter.

That’s why it’s important to include relevant keywords on your resume. You can find these words by reading through the job posting and including them in your resume where they are most relevant. Here are some of the most common keywords for machine learning engineer positions:

  • Machine Learning
  • Python (Programming Language)
  • Deep Learning
  • Python Natural Language Toolkit (NLTK)
  • Machine Learning Research
  • Scikit-learn
  • Artificial Intelligence (AI)
  • TensorFlow
  • Algorithms
  • Artificial Neural Networks (ANN)
  • Statistics
  • Statistics Software
  • Data Science
  • Deep Learning Libraries
  • Machine Learning Algorithms
  • R (Programming Language)
  • OpenCV
  • TensorFlow.js
  • Predictive Analytics
  • Predictive Modeling
  • Data Science V Studio
  • Hive
  • Hadoop
  • Spark
  • Scala
  • Python (Programming Language) Programming
  • Apache Spark
  • Microservices
  • Java
  • Git

Showcase Your Technical Skills

Similar to data scientists, machine learning engineers need to be proficient in a variety of software programs in order to do their jobs effectively. Programs like R, SAS, MATLAB, and SPSS are essential for machine learning engineers, as they allow them to build models and train algorithms. Additionally, machine learning engineers need to be familiar with big data platforms like Hadoop and Spark.

So if you have experience with any of these programs or platforms, be sure to list them on your resume. And if you’re not familiar with them, now is the time to learn them!


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