How to Become a Machine Learning Engineer

Are you considering a career as a machine learning engineer? Are you curious about transitioning from software engineering or data science into an ML role?

This career guide provides an overview of the machine learning engineer role, top hiring companies in the field, and advice on how to land a job.

Below, we summarize a conversation with Nico Thiebaut, a machine learning engineer at Hired, the job search marketplace.

Becoming a Machine Learning Engineer

At a high level, here's what's required to land a job as a machine learning engineer at a top tech company.

Step 1

First, get a bachelor's degree in computer science or data science. A strong math, science, and problem-solving background is important for every data role.

Classes or specializations in statistics are helpful, too.

Step 2

Then, get work experience as a software engineer, data scientist, data engineer, or junior MLE. Writing clean code, processing data, and deploying systems to production will be helpful in your future roles.

Collaborating with data scientists and product teams will prepare you for more advanced ML roles.

Step 3

Develop your experience with personal projects, open-source libraries, and online learning.

Machine learning evolves rapidly, so staying current on the latest tools, frameworks (e.g., TensorFlow, PyTorch), and methods (e.g., MLOps) is essential for career growth.

Step 4

Pursue a master’s or PhD in machine learning, data science, or a related field to increase your chances of landing a role at top companies like Google, Meta, and Amazon.

These companies are heavily invested in cutting-edge ML research, and advanced education is needed to work on complex projects such as deep learning and AI research.

Role Overview

Machine learning engineers are software engineers who develop software that can learn from data and make predictions.

Companies like Google, Microsoft, Amazon, and Meta hire machine learning engineers because they need them to develop algorithms to find patterns in data, make predictions, detect fraud, and improve user outcomes.

This work helps businesses make more informed decisions about best serving users, saving time and money, and gaining competitive advantages with access to large datasets.

Responsibilities

The primary goal of a machine learning engineer is to convert data into actionable insights and products.

Machine learning engineers design, develop, and maintain algorithms and models that make predictions from data.

This OpenAI job posting for an ML engineer highlights some of these key responsibilities:

• Construct a robust pipeline for data management, model training, and deployment to enhance the Moderation model consistently.
• Design, develop, and sustain a robust, scalable data management pipeline. Establish standards for versioning and data quality control.
• Ensure the pipeline can handle data relabeling requests stemming from content policy changes.
• Develop a pipeline for automated model training, evaluation, and deployment with an active learning process and calibration and validation data refresh routines.
• Collaborate closely with stakeholders from product, engineering, and content policy departments to improve moderation models long-term. This applies to external releases and internal use cases across various projects focused on model safety.
• Research the latest techniques and methods in deep learning and natural language processing to enhance the moderation model across unwanted content categories.

Skills and Qualifications

MLEs collaborate with data scientists and software engineers to implement algorithms and models, which helps them collect and process data more efficiently.

Technical Skills

1. Programming Languages: Python, R, Java, and C++ are commonly used in machine learning roles.
2. Mathematics, Probability, and Statistics: These disciplines will help you create and test machine learning models.
3. Algorithms and Frameworks: You don't need to build machine learning algorithms from the ground up; you should understand them thoroughly. Get to know frameworks like Scikit-learn, TensorFlow, and PyTorch.
4. Software Engineering and System Design: Understand software engineering principles and best practices for building large systems.
5. MLOps: This is about making the process of getting machine learning models to production more efficient. You should know about tools and practices for deploying, monitoring, and maintaining these systems.

Soft Skills

1. Communication: You need to communicate well. You'll work with different people, such as data scientists, product teams, and engineers, and you'll need to explain complex technical concepts to them.
2. Problem-Solving: You'll be solving challenging problems in machine learning projects. It would be best to have strong problem-solving skills and the ability to think critically and creatively.
3. Fast Learner: Things change quickly in machine learning. You need to love learning and keep up with new tools and methods.

ML Career Paths

Here are some common career paths for ML engineers, scientists, and data professionals.

Machine Learning Engineer

A machine learning engineer designs and builds machine learning systems by selecting the right algorithms, optimizing them for performance, and integrating them into existing platforms. They collaborate with data scientists, software developers, and product teams to deploy models across various applications like natural language processing, computer vision, and recommendation systems.

For example, a sample Machine Learning Engineer 2 job posting from Amazon lists these requirements:

• 3+ years of professional software development experience, with at least 2 years of system design and architecture experience.
• Proficiency in programming, machine learning, data mining, information retrieval, statistics, or natural language processing.
• Responsibilities include designing and optimizing machine learning systems, building large-scale data processing pipelines, and improving model performance to enhance customer experiences.

Data Scientist

Data scientists analyze and interpret complex data to provide insights that inform business decisions.

They use machine learning algorithms to identify patterns and build predictive models, working closely with stakeholders to solve business problems.

A strong foundation in mathematics, statistics, and computer science is essential for this role.

For example, a sample Data Scientist 2 job posting from Pinterest lists these requirements:

• 4+ years of experience in a data-driven environment, fluency in SQL and Python or R, and proficiency in frameworks like PyTorch, TensorFlow, or scikit-learn.
• Developing analysis pipelines, collaborating with cross-functional teams to improve product experiences, and applying scientific methods to solve complex problems impacting millions of users worldwide.

Machine Learning Researcher

Machine learning researchers develop new algorithms and techniques, pushing the boundaries of artificial intelligence and machine learning.

Whether in academia or industry, they focus on advancing the field and typically hold a PhD in computer science or a related discipline.

For example, a sample ML Researcher position at Apple lists these requirements:

• Machine Learning Researcher to work on long-term, foundational research projects focused on Multimodal Large Language Models (LLMs) and AI Agents.
• Propose and co-develop innovative research, implement experiments, and collaborate with engineers to build scalable solutions for Apple’s future products.
• PhD or MS in computer science or a related field, expertise in machine learning, and experience with deep learning toolkits like PyTorch or Jax.

Data Engineer

Data engineers build and maintain the infrastructure that supports data analysis and machine learning.

They create data pipelines, manage databases, and ensure that data is reliable and accessible.

Data engineers work closely with data scientists and machine learning engineers to make sure data is usable for machine learning projects.

For example, a sample Data Engineer job at Meta lists these requirements:

• Managing and optimizing data warehouse solutions to address business problems and enhance user experience for over 3 billion users.
• Collaborating with product managers, engineers, and data scientists to meet data needs, build and launch new data models, and ensure data quality, privacy, and compliance.
• 2+ years of experience in data engineering, with expertise in SQL, ETL processes, data modeling, and programming languages such as Python or C++.
• Experience with large-scale data processing, data warehousing, and data visualization tools.

Becoming a Machine Learning Engineer

Becoming an MLE isn't an easy endeavor.

Get Educated

The educational requirements for a machine learning engineer position vary depending on the company or institution.

For instance, Google's ML job requirements are as follows. They're a good proxy for what to expect in most similar roles:

• Bachelor's degree or equivalent practical experience
• Eight years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript)
• 3 years of experience in a technical leadership role, overseeing strategic projects, with 2 years of experience in a people management, supervision/team leadership role
• Experience in Machine Learning Infrastructure
• Review code developed by other engineers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency)

Some companies don't require any specific educational background. Still, they prefer candidates with experience in data science, statistics, machine learning, and artificial intelligence.

But at the minimum, you should ideally have a computer science, statistics, or mathematics degree.

Develop the Necessary Skills

Fundamentals of Computer Science

Because machine learning is a subfield of artificial intelligence, you can't get around having a strong knowledge of computer science fundamentals:

• Understanding the essential components of computer architecture, such as bandwidth, memory, cache, memory bus, and CPU architecture, is necessary to design more efficient implementations.
• A solid understanding of data structures such as stacks, trees, lists, queues, arrays, and graphs can help you write efficient code for different use cases.
• Building machine learning models requires familiarity with different types of computational algorithms, such as neural networks, dynamic programming, and hash tables.

Data Modeling and Evaluation

Data modeling and evaluation skills help make sense of data that would otherwise be difficult to interpret.

It also helps build more robust and accurate models than those without data modeling.

For example, in machine learning, data modeling is used for:

• Transforming raw data into a structured format that can be used by machine learning algorithms.
• Finding patterns in large datasets, identifying correlations, and making subsequent predictions.
• Creating data models that predict future outcomes on various topics such as marketing, fraud detection, customer service, etc.
• Building better models and making them more accurate.

Probability and Statistics

Statistical measures such as conditional probability, decision processes, mean, median, and variance help machine learning by providing estimates of the relative likelihood of different outcomes.

Likewise, probability measures help machine learning engineers understand the decision-making process.

Machine learning is heavily influenced by statistics and probability, as they help improve the performance and accuracy of predictive models.

System Design

Machine learning engineers must consider the real-world aspects of producitonizing their ML models.

Good ML system design includes efficiency, monitoring, preventing harmful outputs, and building inference infrastructure.

Designing these systems also requires considering the business problems and limitations of deploying new models.

More Resources

We've connected thousands of job seekers in countless tech roles with expert-level courses and resources to prepare them for their upcoming interviews.

It is impossible to cover all the possible questions since statistics is a vast subject.

Hopefully, these questions have given you a glimpse into what to expect in your data science interviews.

• Explore dozens of mock interviews and practice lessons in our complete machine learning interview course.
• Schedule a free mock interview session to practice answering questions with other peers.
• Get ML interviewing coaching from scientists at top companies with numerous years of experience.