How To Become A Machine Learning Engineer: A Career Guide

Indeed Editorial Team

Updated 30 September 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

A machine learning engineer plays a crucial role in designing and developing machine learning systems and applications. The primary responsibilities of this role include assessing and organising data, implementing tests and experiments and monitoring the performance of machine learning systems. If you have a passion for coding, math, statistics and web development, you can consider becoming a machine learning engineer. In this article, we discuss how to become a machine learning engineer and share the average salary information for the profession.

Related: How Much Do Machine Learning Experts Make? (With Job Info)

How to become a machine learning engineer

Machine learning (ML) is a relatively new tech domain, and not many aspirants know how to become a machine learning engineer. The steps below outline how you can gain the necessary skills and qualifications to build a career in machine learning.

1. Define your end goal

Before pursuing a master's degree or enrolling in an online machine learning boot camp, have a clear idea of your end goal to determine the best path to take. For example, some jobs require candidates to hold a bachelor's degree in computer science, statistics or mathematics. In contrast, other employers may prefer candidates with advanced degrees like a master's in computer science or a PhD in statistics. A few companies determine the eligibility of candidates based on their skills and work experience. So, understanding your end goal helps you choose the right degrees and certifications.

Related: What Is Machine Learning? (Skills, Jobs And Salaries)

2. Become proficient in coding

Most machine learning engineers and data scientists use Python and C++ for coding requirements. Building your coding proficiency in these two languages and their associated libraries can give you an advantage in the workplace. Also, understanding the basics of Github and SQL make it easy to access company data from the servers and work collaboratively with your team.

Here are other programming languages that you can consider learning to build a career in machine learning:

  • TensorFlow software library: This provides an easy framework for users to write code in Java, Python, Swift and C++ for a wide range of deep learning tasks like speech and image recognition. It has extensive documentation and tutorials that make it easy for beginners to start coding.

  • PyTorch: This framework helps developers build their skills in imperative programming models and is popular among the ML community for its ease of use. It allows machine learning engineers to use Python coding to implement deep neural networks.

  • R: It is a free, open-source programming language that offers top-notch, domain-specific statistical and data visualisation applications, including non-linear regression, neural networks and advanced plotting.

  • Structure Query Language (SQL): It is a domain-specific language for storing and retrieving data from relational databases. If your role involves working with databases, you can consider building your knowledge in SQL queries for updating, editing, manipulating and retrieving data.

  • Java: Though one of the oldest programming languages, companies still use Java programmes for modern technologies like machine learning. Developers use Java to create and maintain back-end systems, desktop, mobile and web applications.

  • Scala: Flexible and user-friendly, Scala is a popular programming language for handling large volumes of data. Combining the features of object-oriented and functional programming, Scala delivers high-performance for cluster computing applications.

  • Julia: A modern programming language, Julia has impressive features like simplicity, fast performance and easy readability. Machine learning engineers use Julia for numerical analysis and computational science for solving complex mathematical operations in the finance industry.

  • MATLAB: Data scientists and ML engineers use MATLAB for handling high-level mathematical computations. It is a popular numerical computing language for statistical analysis.

Related: What Is Coding? A Complete Guide To Coding Languages

3. Join a machine learning course

Taking an online course or attending a boot camp helps you build your practical knowledge and gain experience in machine learning. It also enables you to develop a comprehensive understanding of the subject in a short period with practical projects and activities. Several courses also provide industry-recognised certificates that give you an advantage while applying for machine learning jobs. There are several machine learning courses available both online and offline. You can choose a course that suits your career goals and requirements and helps you develop relevant skills.

Some popular machine learning courses include:

  • Machine Learning with TensorFlow on Google Cloud Specialisation

  • Professional Certificate in Machine Learning and Artificial Intelligence

  • Machine Learning with Python

  • Machine Learning from Stanford School of Engineering

  • Professional Certificate in Data Science Foundations

4. Complete a machine learning project

Doing machine learning projects on your own allows you to practice and showcase the skills you have acquired via the course or boot camp. Initially, you can use online machine learning communities like GitHub to get project ideas and recreate existing ones. Once you build your knowledge machine learning, you can create your own projects that you can list on your resume. This is an excellent way to differentiate your resume while applying for jobs and demonstrate practical skills.

5. Improve your data acquisition skills

Machine learning involves teaching systems to process and analyse large amounts of data in real-time. As a machine learning engineer, you are likely to work on projects that handle large volumes of data like marketing, analysis and customer service. Familiarising yourself with writing code that handles large volumes of data can give you an advantage over your peers at the workplace and while applying for jobs. You can improve your data skills by working with large databases and query languages.

6. Become an active part of online machine learning communities

Make use of online communities for machine learning engineers and data scientists that help you connect and interact with your peers in the industry. You can communicate and learn from other machine learning engineers, improving your skills and expertise in the area while staying updated. These communities also host a variety of machine learning challenges where young engineers can test their skills against experienced ML engineers and gain extensive practical experience. These communities can also provide access to online data sets that you can use for your machine learning projects and help expand your network.

7. Apply for a machine learning internship

An internship in machine learning helps you improve in-demand technical skills, develop professional ethics and build soft skills like effective communication, teamwork and collaboration. It also allows you to work on several live projects for different clients. Once you complete the training, you can secure an entry-level job in the industry by showcasing the practical skills and experience gained during the internship programme.

Related: How To Apply For An Internship (And What To Do After Applying)

8. Prepare for a machine learning interview

Irrespective of whether you are looking to work as a freelance engineer or considering a full-time position in a company, these tips can help you prepare for a machine learning interview:

  • Prepare for a practical coding interview. The interviewer usually asks candidates to solve a coding question during the interview. Prepare for the written interview by revising the basics of coding in a programming language you know well.

  • Showcase your specialised machine learning skills. Every machine learning engineer has a different set of skills. Quantify your specialities in a sub-field like deep learning models, image recognition, natural language processing or any other area that interests you by showcasing examples of previously completed projects.

  • Expect a whiteboard data challenge. Sometimes hiring managers can ask candidates to whiteboard a business use case to understand the candidate's thought process and coding knowledge and propose an optimal machine learning solution for a real-world business problem. Prepare for a whiteboard challenge by reviewing diverse online machine learning projects and taking inspiration from them to design your own solution.

  • Keep yourself updated. As machine learning is a nascent field that is rapidly evolving, you can consider additional courses or professional certifications to keep yourself up-to-date with all the latest changes occurring in the industry. Subscribe to machine learning newsletters, periodicals and journals to learn about new algorithms, platforms, tools and other developments in the field.

Related: 60 Machine Learning Interview Questions (With Sample Answers)

Salary of a machine learning engineer

Machine learning is an upcoming field, and qualified and experienced machine learning professionals can earn have many opportunities. The average base salary of a machine learning engineer is ₹62,579. Machine learning engineers have the potential to earn higher salaries based on their experience and location. For example, the approximate average salary of machine learning engineers in Mumbai is ₹90,833. This is considerably higher than the average salary of machine learning engineers in Coimbatore, which is ₹63,333.

Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed. Salary figures reflect data listed on Indeed Salaries at time of writing. Salaries may vary depending on the hiring organisation and a candidate's experience, academic background and location.

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