What’s the Difference Between ML Research and ML Engineering?
By
Samantha Cox
•
Jun 26, 2025
Machine learning research is where the future of AI begins. It’s the behind-the-scenes work that powers breakthroughs in everything from healthcare to finance. While ML engineers build systems, ML researchers ask the big questions, pushing the boundaries of what machines can learn and do. In this guide, we’ll explore what machine learning research is all about, what skills and education it takes, and how it stacks up against ML engineering.
Key Takeaways
Machine Learning (ML) researchers focus on theoretical advancements and contribute to developing new algorithms, while ML engineers implement those algorithms in real-world applications.
Key skills for ML researchers include strong programming abilities and a foundation in mathematics, while ML engineers require robust software engineering and deployment skills.
The demand for ML professionals is expected to grow substantially, creating diverse career opportunities across various industries, including academia, private sector, and technology startups.
The Role of ML Research in Modern AI
Machine learning has seen a resurgence thanks to the increasing availability of diverse big data and advancements in computing power. This resurgence has led to the automatic production of models capable of handling large and complex datasets efficiently through data processing, resulting in faster and more accurate outcomes. Unsupervised learning research is pivotal in driving advancements in machine learning systems and machine learning infrastructure, pushing the boundaries of what’s possible in AI.
These advancements are more than just technological feats; they are the bedrock upon which modern AI applications are built. Innovations from machine learning and ai and ml research have a profound impact across sectors, from enhancing medical diagnostics to revolutionizing financial forecasting. Understanding the immense value that ML research brings to the table is essential.
Key Responsibilities of Machine Learning Researchers
Machine learning researchers design new algorithms and methods to solve complex AI problems. Their goal is to create innovative, effective solutions. To do this, they work with large raw data sets; cleaning, processing, and analyzing the data to uncover insights that guide their research.
ML researchers:
Build and train predictive models for predictions or classifications using predictive modeling.
Conduct experiments to test hypotheses.
Stay current with field advancements.
Continuously optimize how they affect model performance.
Constant iteration and improvement advance the state-of-the-art in machine learning.
Essential Skills for Machine Learning Researchers
Excelling as a machine learning researcher requires a robust skill set. Strong programming skills in programming languages like Python, TensorFlow, or PyTorch is crucial for implementing solutions and conducting experiments. A strong foundation in mathematics, particularly linear algebra, calculus, probability, and statistics, underpins many machine learning algorithms.
Key aspects of working in AI research include:
Collaboration: Researchers often work with data scientists, engineers, and interdisciplinary teams to develop comprehensive solutions.
Flexibility and diverse skill set: Non-technical backgrounds are also valuable in AI roles.
Understanding neural networks: This is fundamental for deep learning applications, which are increasingly prevalent in cutting-edge research.
Educational Pathways to Becoming a Machine Learning Researcher
A machine learning career typically begins with:
A bachelor’s degree in computer science or a related field.
Many ML researchers come from academic backgrounds and often hold advanced degrees like PhDs.
A solid foundation in programming, particularly in Python, which is crucial and often covered in professional certificates related to data science.
Certifications can bolster machine learning expertise in AI. The IBM Machine Learning Professional Certificate covers essential topics like machine learning algorithms, data science, deep learning, training data, machine learning skills, and machine learning concepts, making it ideal for machine learning professionals.
The Harvard Data Science: Machine Learning certificate focuses on methodologies and real-world applications, while Stanford’s AI Graduate Certificate delves into advanced topics like neural networks and reinforcement learning. Programs like Deep Learning.AI’s Machine Learning Specialization are invaluable for building deep expertise.
Comparing ML Research and ML Engineering
While machine learning research and machine learning engineering are both integral to the AI ecosystem, AI engineers serve different roles. Researchers focus on theoretical aspects and algorithm design, while an ai engineer implements these algorithms in practical applications. Understanding these differences is crucial for anyone considering a career in either field or looking to transition between them.
Let’s break down the nuances in their day-to-day tasks, required skill sets, and career opportunities.
Differences in Day-to-Day Tasks
The daily tasks of ML researchers and engineers differ distinctly. ML researchers typically produce detailed research papers advancing theoretical knowledge. They often work independently or in smaller teams, developing models in controlled, theoretical settings. Their work focuses less on immediate application and more on pushing the boundaries of machine learning.
In contrast, ML engineers deploy models in production environments. They work within larger, dynamic teams to address evolving data needs and ensure models operate efficiently in real-world settings. Their deliverables are engineered solutions leveraging models developed by researchers, including aspects of feature engineering. This practical application sets ML engineering apart from the theoretical focus of ML engineers focus and ML research.
Required Skill Sets
ML researchers need the following skills:
Strong problem-solving skills to develop innovative solutions
Effective communication and teamwork
Deep analytical skills
A thorough understanding of statistical methods
Conversely, ML engineers require robust software engineering skills. They must be proficient in software development and deployment technologies like Docker and cloud computing. Understanding data engineering principles is essential for implementing ML models in production environments. This combination of technical skills is necessary for applying machine learning in real-world scenarios, especially for a machine learning engineer.
Career Paths and Opportunities
Career opportunities in machine learning are vast and growing:
Job openings in machine learning are expected to grow by 40% by 2027, reflecting increasing demand for skilled professionals.
The global machine learning market is projected to reach $225.91 billion by 2030.
This growth opens diverse career paths, including roles in academia, the private sector, and tech startups.
Specific roles include:
Data scientists, who are analyzing data and applying machine learning models.
Data engineers, who design and maintain data management systems.
Business intelligence developers, who build systems for data-driven decision-making.
The increasing capability of AI systems drives demand for enhanced automation in routine tasks, expanding career opportunities.
Transitioning Between ML Research and ML Engineering
Transitioning between machine learning research and engineering involves navigating different yet complementary skill sets and career paths:
ML research emphasizes theoretical advancements and algorithm development.
ML engineering focuses on practical application and deployment.
Researchers spend more time conducting experiments and publishing papers.
Engineers focus on building and maintaining systems.
The required skill set for each role reflects these differences. Researchers need deep statistical knowledge and experimentation skills, while software engineers need expertise in software development, computer architecture, and software developers in data operations.
Despite these differences, transitioning between the two fields is possible and offers unique opportunities for career advancement and specialization.
How Companies Use AI Responsibly in Hiring
Machine learning and AI have revolutionized industries by uncovering real-time insights, improving operational efficiency, and providing a competitive edge. In hiring, AI enhances decision-making by matching candidates based on skills and qualifications rather than traditional criteria. This expands the talent pool and reaches diverse candidates who might not engage through conventional application channels.
Fonzi integrates with Applicant Tracking Systems (ATS) to streamline recruitment by:
Reducing manual errors and biases during candidate evaluations
Prioritizing skills and relevant experience over demographic information
Creating a more diverse candidate pool
Ensuring a fairer hiring process
This responsible use of AI underscores the importance of ethical practices in leveraging technology for recruitment.
Introduction to Fonzi: A Curated Talent Marketplace
Fonzi is a curated talent marketplace connecting candidates with elite AI companies and startups. The platform is free for candidates, ensuring transparency in job offers without hidden fees. Fonzi enhances the candidate experience with personalized job recommendations and timely updates, making the job search smoother and more efficient.
For companies, Fonzi charges a success fee only after a successful hire, creating a strong hiring incentive. This model ensures both candidates and companies benefit, making it a valuable resource in the competitive AI job market.
How Fonzi Reduces Bias and Protects Candidate Experience
Fonzi’s AI supports diversity efforts with unbiased candidate evaluations based on objective criteria. Structured interviews involve asking all candidates the same questions, focusing on skills and experiences rather than subjective impressions. This approach reduces unconscious biases and ensures a fairer evaluation process.
Fonzi promotes diverse interview panels to incorporate varied perspectives in decision-making. Continuous monitoring and feedback allow real-time adjustments to recruitment strategies, ensuring the process remains fair and effective.
This commitment to reducing bias and protecting candidate experience sets Fonzi apart as a leader in ethical AI hiring.
Connecting Talent with Top Companies
Fonzi’s Match Day is a 48-hour recruitment event with the following features:
Companies submit salary-backed offers to selected candidates.
Candidates can receive multiple job offers from top companies with just one application.
During Match Day, candidates are assigned a Concierge Recruiter for guidance throughout the hiring process.
Companies in Fonzi’s Match Day gain access to pre-vetted candidates ready to interview, streamlining the recruitment process. This event exemplifies Fonzi’s commitment to connecting top talent with leading AI companies efficiently.
Practical Tips for AI Job Seekers
Key strategies for job seekers include:
Building a robust portfolio with real-world projects to demonstrate practical experience to potential employers.
Proactively solving industry-related problems to stand out to recruiters.
Networking with industry professionals through social media, forums, and events to gain valuable insights and opportunities during the job search.
Understanding market demands by researching job postings and industry reports helps tailor your resume and skills to align with employer expectations. Preparing for interviews should involve practicing both technical questions and soft skills, emphasizing effective communication.
Using AI tools ethically and maintaining a personal touch in applications builds trust in the hiring process through natural language processing.
Summary
Understanding the differences between machine learning research and engineering is vital for anyone looking to enter or transition within the field. While researchers focus on theoretical advancements and algorithm development, engineers apply these algorithms to solve real-world problems. Both roles require unique skill sets and offer diverse career opportunities.
Using AI responsibly in hiring, as exemplified by Fonzi, helps create a fairer and more efficient recruitment process. By prioritizing skills and experience over traditional criteria, and reducing bias, Fonzi ensures that both candidates and companies benefit. As you navigate your machine learning career, remember to build a strong portfolio, network effectively, and stay current with industry trends.
