Career Guidance

Your Roadmap to Becoming a Machine Learning Engineer In 2023

By Shahrukh Zahir | October 17, 2023

Closeup of glasses with code

Machine Learning (ML) is a very quickly growing field in today’s digital age, and it allows systems to use data to learn and improve their decision-making skills without having to rely on explicit programming. A machine learning engineer is critical for this field, and they use data analysis, computer science, and domain expertise to design, implement, and set up intelligent algorithms. These engineers are behind the innovations that work to reshape industries, like self-driving cars, e-commerce recommendation systems, and predicting stock market trends. This roadmap will guide you from beginner to expert, highlighting everything you need to know to thrive in this profession.

What is a Machine Learning Engineer?

A machine learning engineer designs predictive models, software, algorithms, and programs to help machines spot patterns. When they do, the machine learning engineer lays the groundwork to allow the machines to take independent actions and perform specific tasks without additional direction.

Related: Transferable Tech Skills

What do they do?

Machine learning engineers are vital members of a data science team. They have various tasks that involve building, researching, and designing the artificial intelligence system responsible for machine learning. These engineers also work to maintain and improve existing AI systems.

Usually, a machine learning engineer is the primary communicator among the other data science team members. They work directly with other data scientists to create models to build AI systems, and they communicate with the teams that take this model and build and run it. The job can vary, but it usually involves setting up machine learning algorithms, running tests and experiments with AI systems, performing statistical analyses, and developing and designing systems for machine learning. 

Are you a company that is struggling to wade through hundreds of applications to find the best talent to fit your team? Right Fit Advisors encourages you to reach out and allow us to use our candidate sourcing and screening process to match you with the best candidates for your positions. 

The Machine Learning Engineer’s Role and Responsibilities 

Mainly, this position has to develop and design various machine learning systems. This includes putting in the correct machine learning algorithms and running experiments to see if it works as they think it will. Ideally, this person will have to have strong programming skills, a lot of data science knowledge, and be very good at statistics. 

This position’s ultimate goal is to create efficient self-learning applications that can evolve and adapt as time goes on to push AI technology’s boundaries. You may also have to: 

  • Train and retrain systems as needed
    • Extend current machine learning framework and libraries 
      • Stay on top of new field developments 
        • Fine-tune the machine learning applications based on statistical analysis results 

Related: Navigating the Future: A Guide to AI in Tech Recruitment

Laptop with code on a desk

How to Become a Machine Learning Engineer

To become a machine learning engineer, there are a few helpful steps to give you a competitive edge. These steps include:

1. Learn a Programming Language

To become a ML engineer, you have to learn a programming language. Python is a great beginner option because it has a vast library ecosystem and is simple. R is another solid pick for graphics and statistical computing. As a beginner, you want to understand the fundamental concepts of your chosen language, like loops, data types, functions, and conditional statements.

As you improve, you should familiarize yourself with ML-centric libraries, including pandas, NumPy, and Scikit-learn. Trying out deep learning frameworks like PyTorch and TensorFlow may also be a good idea. Learning any programming language takes practice, and you can do small projects, hands-on coding, and coding challenges to help you learn

2. Revisit Mathematics

Most areas of machine learning use mathematical concepts to develop the programs and algorithms needed for the machine to make independent actions. Many algorithms use linear algebra to form the foundation, addressing matrices, vectors, eigenvalues, and eigenvectors. You need to know differential calculus to understand how to optimize your algorithms because this helps you leave room for parameter adjustments to model outcomes and reduce predictions.

Statistics and probability, including standard deviation, median, mean, and Bayes’ theorem, will help you understand and predict data patterns. Finally, optimization requires you to fine-tune your models using linear algebra and calculus, and you’ll use information theory to understand information gain, entropy, and KL divergence.

3. Develop Machine Learning Skills 

To master machine learning, you’ll combine mathematical insights, programming, and contextual understanding. You have to know the difference between the various paradigms, like unsupervised, supervised, and reinforcement learning, and you have a good grasp of calculus, linear algebra, and statistics. 

Hands-on coding practice is also essential, like using libraries like PyTorch and TensorFlow to go from linear regression to neural networks. You should also learn to use tools like NumPy and pandas to preprocess data and perform transformation and analytical operations. You’ll also need model evaluation skills like F1-score, accuracy, and ROC curves to help you assess and refine your models. 

4. Practice Data Science

Data science is a mix of practical applications and theory. You’ll have to learn how to use specific data exploration tools like Seaborn and Matplotlib, data processing, statistical analysis, test hypotheses, and machine learning libraries like Scikit-learn. Learning by doing hands-on projects and challenging yourself with new problems can help refine your skills. To be successful in this field, you’ll also need a curious mind and be ready to practice. 

5. Build Your Machine Learning Portfolio

A machine learning portfolio is a great way to show your skills and what you learned to prospective employers, and it can also give you a sense of satisfaction. To build this portfolio, pick out diverse projects showcasing your knowledge, from deep learning models to regression analysis. 

Make sure you document your projects because this is a great way to capture your problem-solving skills, methodologies, and results. It’s possible to use a few platforms for storytelling and include personal passion projects or real-world databases as examples. Regularly update your portfolio based on feedback to get the opportunities and professional recognition you deserve. 

6 Ace Your Interview

The interview may be an intense process, but if you know your skills and knowledge, you should ace it. Be prepared for the following:

  • Hands-on coding – Expect prospective employers to want you to demonstrate your coding skills in real time. Before your interview, refresh any algorithms and data structures you don’t use often. Practice and familiarize yourself with the small nuances of your chosen programming language. The goal is to be ready to write clean, efficient code under a timer. 
  • Discuss your specialized skills – Set up your discussion so it loops around your strengths. Whether it’s natural language processing, deep learning, or advanced statistical modeling, showcase any experiences or projects where you had to apply these skills. Also, have a few anecdotes ready that highlight your problem-solving capabilities.  
  • Whiteboard data challenge – This is where problem-solving and your conceptual understanding collide. Your interviewer might ask you to design an algorithm, pull insights from a hypothetical dataset, or explain a machine learning model. Practice breaking down complex problems and being able to explain them step-by-step.

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What if You Don’t Have a College Degree?

Since machine learning is a relatively new field, you don’t necessarily need a degree. Instead, focus on machine learning basics and build a strong foundation in programming languages. Work on personal machine learning projects and build a network of professionals. Finally, stay current in the field by attending conferences, meetups, and online communities.

Becoming a Machine Learning Engineer

Person holding a paper with AI

Are you ready to start on the path to become a machine learning engineer? Maybe you need help finding the perfect company to match your talents. Right Fit Advisors can help match candidates to companies and set you up for success in your new career. 

Related: Succeeding in Remote Tech Jobs

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