When you try to explain your job to people, most of them just think you’re a tech wizard—and they’re not wrong. Working in machine learning, you use complex algorithms to create intelligent systems that learn from data and continually improve over time.
Few industries are as fast-paced as machine learning right now, and there’s a huge demand for experts like you. To get the best offer possible, you’ll need to write a resume that effectively highlights your strengths.
We get it—keeping up with the world of machine learning is quite time-consuming, so we’re here to help you score your next job. Check out our real machine-learning resume examples and tips for writing an effective cover letter!
Why this resume works
- Let’s talk about the resume template and colors you’re choosing. Your machine learning resume goes beyond just work experience and metrics.
- Create a lasting experience for hiring managers by using the “Elegant” template and warm colors that are easy on the eyes. Now, structure your work experience chronologically and add them along with all your best skills on the left-hand side. A simple resume that highlights all your technology expertise is more than enough here!
Why this resume works
- Applying as an intern for such a challenging and competitive field can be challenging. What’s going to make things easy here is your career objective.
- Your employer has to go through hundreds of applicants who are just fresh out of college. There’s only one way your machine learning intern resume can stand out. Clearly align your objective to state your past project experience and contributions along with your reason to apply at the company.
Why this resume works
- If you’re starting but want to make recruiters give you as much attention as the gurus, a tailored career objective strategically placed at the top of your junior machine learning engineer resume is your safest bet.
- Starting your resume with a top-notch career objective is like putting the cherry on top of your narration. Here, you hint at abilities picked from internships and college projects and how you intend to use them to add value to the target company. Think of it as a little whisper you know what you want and are willing to go the extra mile to get it, a mindset that appeals to many recruiters.
Why this resume works
- How quickly were you able to scale your career? If you’re naturally talented at handling systems and made jumps in promotions within years, then it’s worth mentioning.
- Use each work experience to your advantage here. Show your dates for each role to convey how you’ve managed to become a senior in this field within a couple of years. Add more flair to your senior machine learning engineer resume by listing your versatility in using multiple software.
Why this resume works
- The wrong artificial intelligence machine learning resume template can totally tank your chances. But the right one? It gives a shoutout to your professionalism, makes a positive first impression, and shows off in-demand skills like attention to detail.
- You can’t go wrong with a professional template with a two-column layout. Divide all sections clearly, adding 1-inch margins on both sides for a more balanced (and visually appealing) look. And font? Stay with timeless crowd-pleasers like Calibri, Trebuchet, and Cambria.
Why this resume works
- Were you able to score a job within the next year of graduating? That’s exactly what you need in your machine learning engineer resume!
- A good number of candidates spend years after college to land a job. Showcase your passion for this field by adding the first place you worked at, even if it was only as a data analyst. As long as you can pull up relevant metrics, you’ll be all set.
Why this resume works
- What’s really going to make your deep learning resume is the ability to link all your past job roles. You can’t start off as a specialist here and that’s completely fine.
- A small role such as a research assistant can also do wonders if you’re able to mention your impact on the company’s process. Use quantifiable bullet points like, “21.3% improvement in product safety as per risk assessments” that speak volumes about your skills in deep learning right from the get-go.
Related resume examples
Tailor Your Real Machine Learning Resume to Match the Needs of Your Clients
If we only had the room to give you just one tip—that’s right, just the one—it’d be to tailor your resume to perfection for each role. When it comes to machine learning, there’s no better guide for this than following the job responsibilities section of the job description.
Don’t have a specific job in mind just yet? No worries. You can get your resume ready to go by following some of the examples below—this is what most employers look for in a machine learning job!
- Plan, research, and develop end-to-end machine learning products and architectures
- Create software libraries and computer algorithms to support executing machine learning models
- Work with data engineering teams to build and maintain data pipelines
- Create documentation for processes for model retraining, versioning, and deployment
- Design and implement classical autonomy and AI algorithms
- Optimize and scale up machine learning algorithms and software, liaising with internal and external ML teams
Adding skills to your real machine learning resume
15 top real machine learning skills
- TensorFlow
- PyTorch
- Keras
- Apache Kafka
- Apache Hadoop
- Kubernetes
- Pandas
- Jupyter Notebook
- Python/Java
- CUDA
- Git
- AWS/Azure/GCP
- Scikit-Learn
- Docker
- OpenCV
Your real machine learning work experience bullet points
You develop machine learning models and algorithms with frameworks like TensorFlow and PyTorch. Next, you analyze data on Pandas. Lastly, just for good measure, you’ll document some code and model performance. That’s a lot, and that’s just your daily grind.
To distinguish yourself, however, talk about your achievements rather than your day-to-day work. And to make them really catch a recruiter’s eye, back them up with quantifiable metrics.
For instance, talk about how the model you developed on Python improved predictive accuracy for business processes or how much your TensorFlow model reduced algorithm runtimes.
- Highlight how resolving code issues led to improved stability or reduced occurrences of system crashes or data loss.
- Emphasize the time and cost savings that the implementation of your machine learning models resulted in.
- Mention how data processing systems or scripts you implemented improved efficiency, data entry or processing speeds, and reduced manual error checks.
- Discuss how your optimization strategies improved prediction model accuracy, boosted benchmarks, or shrank biases.
See what we mean?
- Resolved complex CUDA code issues, increasing the stability of machine learning applications and reducing incidents of system crashes by 83%
- Innovated with Google AutoML to refine acoustic signal processing in Moog’s testing facilities, improving the detection of ultrasonic frequencies by 79.4%
- Engineered a Flask-based REST API for an analytics dashboard, leading to a 27% increase in back-end efficiency and a 19% reduction in load times
- Implemented an OpenCV-powered video analysis tool that reduced content classification time by 2.6 hours, enhancing content discovery features
9 active verbs to start your real machine learning work experience bullet points
- Engineered
- Implemented
- Designed
- Initiated
- Spearheaded
- Optimized
- Pioneered
- Streamlined
- Collaborated
3 Tips for Writing a Real Machine Learning Resume as a Budding ML Engineer
- Showcase your coding skills
- Programming is a vital part of machine learning. To show recruiters you’re up to the task, highlight your proficiency in commonly used programming languages in the field, such as Python, Java, or R.
- Emphasize your training and education
- If you have a degree in ML, CS, or a related field, highlight this in your resume, including any relevant projects or research you conducted during your studies. This will show employers what you’re capable of.
- Discuss your internships
- Internships can provide you with a lot of valuable work experience, so talk about projects you’ve done that were related to machine learning. Discuss those you participated in and what you were able to learn.
3 Tips for Writing a Real Machine Learning Resume as a Senior Engineer
- Quantify your impact and results
- To make your resume stand out, provide quantifiable metrics and results of your machine learning projects. Talk about how your work had a positive impact on KPIs such as model accuracy, efficiency, or performance, reinforcing your ability to deliver real results.
- Mention your research contributions
- If you’ve worked on any machine learning research or publications, highlight them in your resume. This could be anything from presenting at conferences, co-authoring papers, or being involved in cutting-edge research.
- Elaborate on your specialization
- If you specialize in a niche within machine learning, such as natural language processing, deep learning, or machine learning architecture, dive into its details. Talk about advanced courses, certifications, or workshops you’ve completed to reaffirm your expertise.
Talk about times when you took control of complex machine-learning projects. Discuss how you managed project timelines and made critical decisions, working with both non-technical and technical stakeholders.
Staying current in your line of work is crucial. Show recruiters that you’re up to date by mentioning any conferences, advanced courses, or workshops you’ve attended. Doing this highlights your dedication to your professional growth in the industry.
Try and stick to a single-page resume. If you have more than a decade of experience in machine learning, it’s fine to stretch this to two pages—however, even if this is the case, include only your most recent and relevant experience.
