Introduction

The great talent race is on, and what if I tell you how you can grow your career prospects by acquiring the deep skills companies are desperate for?

Here, I am talking about – Machine Learning Engineer Jobs!

As enterprises automate their procedures, ML is a critical talent in the rapidly growing AI field. No technology can match AI/ML for this reason.

As an introduction to the topic, machine learning (ML) teaches computers to learn from data rather than following instructions. The system “learns” patterns from your many examples. After that, it can forecast,discover anomalies, and decide without human assistance.

You witness the result of this every day. A machine learning model is usually behind Netflix’s movie recommendations, your bank’s suspicious transaction blocking, and your email’s spam folder shift.

Therefore, the ML industry is growing very fast. By 2030, the global ML sector might be worth over $280 billion, growing at 30.4% from $55.80 billion in 2024. Asia-Pacific is one of the fastest-growing markets, while North America leads with a 29.0% share.

From its application in various industries, ML is moving into almost every major industry. For example – Healthcare(medical imaging, disease risk prediction), Manufacturing(predictive maintenance, quality control), eCommerce(product recommendation, sales prediction), Marketing(lead scoring, churn prediction), and more.

The market value is projected to surge from USD 4.5 billion in 2024 to USD 231.54 billion by 2034, reflecting significant long-term growth.

As Kai Fu Lee(AI Expert and CEO of Sinnovation Ventures) said, “I believe AI is going to change the world more than anything in the history of humanity. More than electricity.”

The future of the AI/ML industry has surprises that will be revealed over time, and machine learning engineer jobs will be at their peak. Fortune predicts 1 million new ML specialist jobs by 2027. It means that the correct machine learning engineer career path can lead to long-term, high-impact work with high compensation and growth.

In the next sections, we will look at what a machine learning engineer actually does, which skills you need, and the 25 best machine learning engineer job profiles you should know before you plan your career or hiring strategy.

What Does a Machine Learning Engineer Do?

For businesses that are looking to hire experts or individuals who just want to start their career as machine learning engineer jobs, it’s essential to understand the role and responsibilities. So, what an ML engineer does is turn data and models into working products that help the business.

A machine learning engineer designs and builds ML systems, trains and tests models, and sends them into production so real users can benefit, such as businesses, end customers, and others.

Let’s look into the details through a machine learning engineer job description, which helps you to have a deeper understanding.

An ideal machine learning engineer job description usually includes responsibilities like:

• Understand business challenges and identify issues to solve them through deploying AI.
• Collect, clean, and prepare data from databases, APIs, logs, or third-party tools.
• Choose and build suitable models (for example, tree-based models, neural networks, or recommendation algorithms).
• Train, validate, and tune models to reach good accuracy and stability.
• Work with software engineers to deploy models into production apps or services.
• Monitor model performance, fix issues, and retrain when data changes.
• Write clear documentation and explain results to non-technical stakeholders.

Additionally, an ML engineer also expects to collaborate with Data Scientists, Data Engineers, AI Product Managers, and Business Stakeholders.

Why are Machine Learning Engineer Jobs in Demand?

It is one of the hot topics searched on the internet – why are machine learning engineer jobs increasing?

After examining the reasons, one is that machine learning applications in business are growing every year, requiring someone to design and maintain those systems.

Let’s understand it through some real applications in businesses:

• Sales & Marketing Optimization: Personalized Recommendations, customer segmentation, predictive lead scoring, and focused marketing campaigns.
• Risk Management & Security: Fraud detection, credit scoring, cybersecurity, and faster loan approvals.
• Operations & logistics: Demand forecasting, route optimization, predictive maintenance, and supply chain optimization.
• Product and digital experience: Spam detection, auto tagging, search running, autocomplete, and more.
• Sentiment Analysis: customer behaviour analysis about the brand.

There are a variety of applications that exist and need an ML model, including data pipelines, APIs, monitoring, and retraining logic. That is exactly what machine learning engineer jobs focus on.

Skills Required for Machine Learning Engineering Jobs

Thus, what it needs to succeed in machine learning engineer jobs is a specific set of skills. If you have these skills, it will land you in a good job as an ML engineer or help you hire an expert with relevant skills for your project.

For better learning, prerequisites to learn machine learning are the basics of programming languages such as Python, core libraries such as NumPy, Pandas, etc, mathematical and analytical ability, logical thinking, and most crucially, data handling skills.

So, the required skills include:

• Technical Skills

Machine Learning implementation requires technical skills such as a deep understanding of supervised/unsupervised learning, ML algorithms(regression, classification, clustering, decision trees), model building and training, model evaluation, feature engineering and preprocessing, and MLOps fundamentals.

• Programming Languages

Python is the default language for AI/ML development; however, C++, R, Java, Scala, Lisp, and Prologue also have machine learning competence. Additionally, mastery of libraries like NumPy, Pandas, Scikit – learn, and PyTorch/TensorFlow is essential. These are common prerequisites to learn machine learning, because they help you move from theory to code.

• Data Engineering and Handling

To train the models, data is required, and an ML engineer must have working knowledge of Data Pipelines(Airflow, Prefect, Dagster), Big Data tools (PySpark, Kafka, S3, ADLS), Data quality and validation, and Database Management(SQL and NSQL).

• Cloud Platforms & DevOps

Having knowledge and expertise in at least one major cloud provider, such as AWS, Azure, and Google Cloud Platform, is advantageous.

• Mathematical & Statistical Skills

Although not necessary, knowledge of linear algebra, calculus, probability, and statistics will help.

Additionally, problem-solving, subject knowledge, soft skills, and collaborative skills will boost your employability. Consider 2025 trends to learn Gen AI, Responsible AI, Edge ML, and Automated ML. The best Generative AI courses on major learning platforms teach all these.

How to Become a Machine Learning Engineer?

If you want to know how to become an ML engineer, in simple steps, it’s just like learn the basics, practicing with tools, working on some real projects, and applying for the job.

Here are the steps!

#1. Start with the Right Course

It is a great idea to start with a beginner-friendly machine learning course that covers regression, classification, model evaluation, and overfitting. After that, add a deep learning specialization course to learn neural networks, CNNs, and RNNs.

#2. Learn Essential Tools & Frameworks

For machine learning engineer jobs, it is a must to have proficiency in key tools and frameworks.

For coding, focus on:

• Python, Jupyter Notebook, and Git
• Libraries like NumPy, pandas, and scikit – learn.
• Deep learning frameworks such as TensorFlow or PyTorch

Then add “production” tools over time:

• Docker and basic Linux commands
• At least one cloud platform (AWS, GCP, or Azure)
• An experiment tracking or MLOps tool like MLflow or similar

#3. Develop a Real Project

Start with a small idea and start developing using the knowledge and skills you have gained through the course and training. Test it on real ground and get feedback from an experienced AI trainer. Once done, start with another project. Work on at least 2 – 3 projects.

#4. Build A Portfolio

Build a portfolio, mention your skills, project details showcasing relevance with the job role, add education details, and apply for the job. While applying for jobs, also prepare for interview questions correlating with machine learning engineer jobs.

25 Best Machine Learning Engineer Job Profiles You Need to Know

Here are the top 25 machine learning engineer jobs that companies hire for, based on core, specialized, research-based, and industry-specific roles.

Core ML Roles:

Core machine learning positions refer to the job profiles that perform core ML tasks.

#1. ML Software Engineer

An ML Software Engineer builds and integrates ML features inside real products. They work with data, models, and backend code. Their role and responsibilities include model training, creating APIs, writing clean code, and making sure the model output reaches the app or website correctly. This is one of the most common ML engineer jobs in product-led companies.

With this profile, an individual can start their machine learning engineer career path. As businesses are looking for automation in the future, demand for this role will increase.

Average Salary: $100,000 – $150,000/ year

#2. Senior Machine Learning Engineer

The role of a senior machine learning engineer is a step ahead of the junior role, as they lead complex ML projects. Their responsibilities include designing system architecture, choosing the right models, reviewing code, and guiding junior engineers. They also collaborate with other roles, such as data scientists and product managers, to design and deliver scalable ML services.

With the high demand in diverse industries for machine learning engineer jobs, the future outlook is strong.

Average Salary: $150,000 – $200,000+ /year

#3. Lead Machine Learning Engineer

The lead ML engineer leads a team comprised of junior and senior machine learning engineers and oversees the tasks of the team. Their job role and responsibilities include – Strategy, Leadership, Technical Oversight, and Advanced Expertise. They decide priorities, review designs, and ensure that ML work supports business goals. This is a higher step on the machine learning engineer career path, close to an engineering manager.

Lead roles are in high demand, and the role is promising. Still, there are open positions for lead machine learning engineer job roles. So, it can be a great opportunity for experienced ones.

Average Salary: $200,000 – $300,000+ /year

#4. Principal Machine Learning Engineer

This role is superior to all the roles mentioned in the category of core machine learning positions. Rather than leading a single team, they guide several teams at a time. The principal ML engineer is responsible for the strategic design, development, and deployment of complex, innovative ML systems. They also help create AI and ML engineering jobs within an organization to improve production and utilize human resources.

From the perspective of machine learning positions, as skilled professionals are in demand, this role has a wide future scope and is witnessing rapid growth in 2025.

Average Salary: $300,000 – $500,000+ / year (top 10% earn more than $500k in a year)

Specialized ML Roles:

Specialized machine learning positions offer a different perspective for professional growth in the AI/ML field. Let’s see what these roles are.

#5. NLP (Natural Language Processing) Engineer

NLP engineers are responsible for creating the software or applications that can understand human language and process it for the outcomes. The role is an intersection of computer science, AI, ML, and linguistics. An NLP engineer builds models for chatbots, speech recognition systems, search, sentiment analysis, and more.

With the increasing use of devices like Alexa, Amazon Echo, or Google Nest, demand for NLP experts is dynamically increasing, which creates jobs in machine learning engineer roles.

Average Salary: $120,000 – $150,000+ / year

#6. Computer Vision Engineer

A Computer Vision Engineer works with images and videos. They develop and deliver models for object detection, face recognition, OCR, quality checks, and medical imaging. This role fits people who enjoy visual problems and like to see their models work on real camera data. It’s one of the most popular machine learning positions in industries like robotics, retail, automotive, and healthcare.

As more devices use cameras and sensors, this is one of the most in-demand roles in machine learning engineering jobs. Computer vision roles are growing fast, creating opportunities for experts.

Average Salary: $90,000 – $170,000+ / year

#7. Deep Learning Engineer

The primary role of a deep learning engineer is to design, develop, and implement a neural network model for advanced AI applications. A deep learning engineer is a specialised machine learning position because deep learning succeeds where traditional machine learning fails. They are experts at using frameworks like TensorFlow or PyTorch, large datasets, GPUs, and techniques such as fine‑tuning, distillation, and model compression.

The global deep learning engineering market is set to be worth $279.60 billion by 2032, at a CAGR of 35.0%(2025 – 2032). This will create more deep learning jobs for individuals, offering better career prospects.

Average Salary: $150,000 – $250,000+ / year

#8. Recommendation Systems Engineer

Industries like OTT, Entertainment, and eCommerce are focusing on providing personalized recommendations, and in the background, a recommendation systems engineer works to make this happen. They study user behavior, clicks, and history, and then design models that show the right content at the right time. This is a nice path inside the machine learning engineer career path if you like working with user data.

Because of the integration of the latest technologies to enhance AI system capabilities for personalization, machine learning engineer jobs like recommendation system engineer have a bright future.

Average Salary: $100,000 – $200,000+ / year

#9. Reinforcement Learning Engineer

Reinforcement learning engineering has applications in industries where complex decision-making is much needed, such as optimizing large delivery networks, controlling robotic systems, or in financial trading strategies. A Reinforcement Learning Engineer develops agents that learn by trying actions and seeing rewards. They create an environment of AI and define reward signals to make a model learn how to achieve the target.

Reinforcement learning is still a smaller area, but it is growing as experts are required across industries for sequential decision-making using AI and ML.

Average Salary: $90,000 – $200,000 / year

#10. Machine Learning Platform Engineer

The machine learning platform engineer job role focuses on infrastructure and operational aspects of machine learning. An ML platform engineer develops internal platforms, services, and tools that other ML teams use. Their responsibilities are developing ML systems, implementing data pipelines, automating workflows, infrastructure, and managing infrastructure. They also collaborate with data scientists, engineers, and product managers.

As companies grow with ML, in the future, there will be more openings expected for this machine learning position.

Average Salary: $90,000 – $300,000 / year

#11. ML Infrastructure Engineer

The ML infrastructure engineers are responsible for developing and maintaining the systems that allow AI models to operate on a wide scale. This job role intersects with the roles of machine learning, data science, and DevOps.

This is a long-term role as companies are heavily investing in AI/ML, and they need expert candidates to manage their ML infra.

Average Salary: $95,000 – $300,000+ /year

#12. Feature Engineering Specialist

A Feature Engineering Specialist’s job is to convert raw data into useful features that models can use. They work closely with data, domain experts, and ML engineers. They design how to clean, join, and transform data so models can learn well. Their key tasks involve data exploration, feature creation, feature selection & extraction, and analysis.

The future aspect for this one of the machine learning engineer jobs is positive. Although this role is limited, it is rapidly growing over time.

Average Salary: $100,000 – $150,000+ /year

#13. Model Evaluation Engineer

A Model Evaluation Engineer designs tests and metrics for ML models. Their job role is to check accuracy, fairness, drift, and stability. They also build dashboards and reports so teams can see when a model starts to fail. This role is important in jobs in machine learning engineer teams that work on end-user-focused products.

This role is growing as more companies care about safe and fair AI. With expanding rules for AI safety and bias, there is a growing demand for model evaluation engineers.

Average Salary: $100,000 – $160,000+ /year

#14. MLOps Engineer

An MLOps Engineer handles deployment, CI/CD, monitoring, and retraining for ML models. They set up pipelines, logging, and alerting. They work very closely with both ML engineers and DevOps teams. Many ML engineer jobs now expect some basic MLOps skills. In simple terms, these engineers bridge the gap between operations and data science.

The future is very positive, because without MLOps, ML models do not stay useful in production.

Average Salary: $100,000 – $300,000+ /year

#15. ML Pipeline engineer

An ML Pipeline Engineer’s key role is to design and maintain data and training pipelines to feed models, validation steps, and deployment steps. They handle processes such as ETL processes, batch and streaming workflows, and scheduling the training tasks, to make sure workflows are repeatable and require less manual effort.

This job will stay important as companies move from one-off models to regular training and releases.

Average Salary: $100,000 – $300,000+ /year

#16. ML Reliability Engineer

An ML Reliability Engineer focuses on uptime, failures, risk, and performance of ML systems involved in production. They watch for incidents, set SLOs for models, and plan rollbacks and safety checks. They work a bit like an SRE, but with a focus on ML services.

As ML systems touch more critical parts of business, this role becomes more important. So consider this one of the crucial machine learning engineer jobs for the future.

Average Salary: $160,000 – $180,000+ /year

Research:

Among trending AI and ML engineering jobs, research-based roles are also there, as behind every innovation, there is research. The roles include:

#17. Machine Learning Researcher

As the role implies, researchers innovate new algorithms and design new methods to make the most of machine learning. Their key tasks include advanced research, algorithm and model development, prototyping and testing, documentation and publication, and collaboration with cross-functional teams.

From a future perspective, the world is racing towards innovations in AI, ML, and Deep Learning, so the role of ML researcher is stable and will increase in the upcoming years.

Average Salary: $80,000 – $150,000 (at initial level)

#18. Machine Learning Scientist

This is an advanced role than a researcher and one of the highest-paying machine learning engineer jobs in the industry once the candidate acquires deep knowledge. ML scientists not only research but also design experiments, test model ideas, and work with engineers to bring strong methods into real products.

This is a future-proof machine learning engineer career path and can lead an individual to the top positions in an organization because businesses are investing more in research to produce the products that can change lives.

Average Salary: $150,000 – $300,000+ annually

#19. Research Scientist – Deep Learning

Deep learning scientists focus on more complex tasks, spend their time in research and developing neural networks and algorithms to achieve outcomes when handling complex data, such as advanced image recognition or next-generation language models. So, their key tasks are research, prototyping, problem-solving through DL, and more.

With the soaring demand, this role is increasing in the current year, and in the future, there will be more open positions for it.

Average Salary: $120,000 – $250,000+ /year

#20. AI Research Engineer

An AI Research Engineer turns research ideas into working code and demos. They read papers, build prototypes, and help product teams use new models. They are more hands-on than pure researchers but still close to cutting-edge work.

The future of this role is promising as AI research engineers are in demand to push the boundaries of AI to develop something unique and game-changing.

Average Salary: $100,000 – $300,000+ /year

Industry Specific:

Here are some industry-oriented machine learning engineer jobs, such as:

#21. Healthcare Machine Learning Engineer

A healthcare ML engineer designs AI/ML systems for healthcare businesses, hospitals, clinics, and research labs. Their skills combine software engineering, data science, and healthcare industry knowledge. They design and train systems for tasks like medical image analysis (X-rays, MRIs), disease prediction, and personalized treatment recommendations.

The healthcare industry is now focusing on AI to improve care services and reduce repetitive tasks to save time and cost. It is a growing area for ML engineer jobs as more data appears in digital form.

Average Salary: $90,000 – $200,000+ /year

#22. FinTech Machine Learning Engineer

A fintech machine learning engineer is a specialized role, and these are the professionals who develop AI/ML systems for the fintech industry or businesses to automate processes, manage risk, and personalize services. They also take care of security and compliance to ensure ML solutions adhere to the regulatory standards and data privacy.

Around 85% of financial institutions are projected to adopt AI, and in the future, this ratio will increase. This is one of the strongest areas for jobs for machine learning engineer candidates.

Average Salary: $150,000 – $200,000+ /year

#23. Autonomous Driving ML Engineer

An Autonomous Driving ML Engineer designs, develops, and delivers models for self-driving or driver assist systems. Including machine learning, they also utilize computer vision and data sciences to make an autonomous vehicle system learn to control vehicle functions such as path identification, steering, braking, and acceleration.

The autonomous vehicle market is the hot segment and projected to grow $1,730.4 billion by 2033 at a CAGR of 31.85% (2025-2033). Consider it one of the best machine learning engineer jobs.

Average Salary: $90,000 – $250,000+ / year

#24. Robotics Machine Learning Engineer

A Robotics Machine Learning Engineer builds models that help robots move, see, and act. They may work on warehouses, factories, drones, or home robots. They join sensor data, control, and ML to create useful behavior.

With the widespread use of AI-powered automation, the roles for robotics engineers are exponentially rising. The future is bright, and more open positions will be available in the upcoming five years.

Average Salary: $150,000 – $200,000+ /year

#25. Fraud Detection ML Engineer

A Fraud Detection ML Engineer builds models that catch fake or risky behavior. Their core role is to design and develop the systems that use AI and Machine Learning to identify fraud in real-time. The tasks include model development and deployment, data analysis, feature engineering, performance monitoring, and others.

From a future perspective, this role is suitable for all industries, from fintech to e-commerce, and in the upcoming years, the employability of fraud detection engineers will increase.

Average Salary: $150,000 – $250,000+ /year

How to Choose the Right Machine Learning Job Profile

Till this section, you have accessed very crucial information about the machine learning engineer jobs, their roles, responsibilities, future, and average salary. Still, for some readers, it may be confusing to choose which role. Thus, it’s like:

#1. Based on Skills

• If you enjoy coding and developing systems, then core ML engineer roles like ML Software Engineer, MLOps Engineer, or ML Infrastructure Engineer are for you.
• If you are an expert in mathematics and have the curiosity to deepen your knowledge, roles such as Machine Learning Scientist, Research roles, or Deep Learning Engineer are suitable.
• If you have a zeal for creating new features and want to play with the data, roles like Feature Engineering Specialist or Model Evaluation Engineer fit well.

#2. Based on the Industry

AI and Machine Learning have applications across industries that create plenty of artificial intelligence engineer jobs. Thus, based on your industry choice, you can start your career path, such as a Healthcare machine learning engineer, Fintech ML engineer, or other.

#3. Based on Experience Level

Experienced-level ML engineer job roles require years of expertise, so you can start with entry roles like ML Software Engineer, AI Trainer, or Data / ML Pipeline roles. Once you have experience, you can move to the upper hierarchy, such as Principal ML Engineer, Senior ML Scientist, or Senior Deep Learning Scientist.

Read Also: Artificial Intelligence Jobs

Salary Trends for Machine Learning Engineers in 2025

The average machine learning engineer salary in 2025 is $120,000 – $170,000 annually. However, region-wise, it differs. Such as:

#1. USA

The average salary range for machine learning jobs in the USA is

• Entry-Level: $105,000 – $150,000/ year
• Mid-Level: $150,000 – $200,000 /year
• Senior-Level: $200,000 – $350,000+ /year

#2. India

If you want to hire machine learning engineer in India, consider the following salary range:

• Entry-Level: ₹6–10 LPA
• Mid-Level: ₹12–20 LPA
• Senior-Level: ₹20–35 LPA

#3. UK

Including entry-level AI jobs, the UK also hosts other roles, and the salary range is:

• Entry-Level: £35,000 for graduates/year
• Mid-Level : £50,000 to £80,000 / year
• Senior-Level: £120,000+ / year

#4. Remote Roles

Remote machine learning jobs offer opportunities for individuals across the globe, and the salary range for 2025 is:

• Entry-Level: $80,000 – $150,000 / year
• Early Career: $120,000 – $190,000 / year
• Mid-Level: $170,000 – $320,000 / year
• Senior Level: $250,000 – $500,000+

Career Growth Path for ML Engineers

A machine learning engineer’s career usually moves through three stages: entry, mid, and senior / leadership.

Entry-level roles:

Individuals start entry-level machine learning jobs such as Junior ML Engineer, ML Software Engineer, Data Scientist (junior), or even AI Trainer, focusing on learning tools, data handling, small models development, and other basic tasks.

Mid-level roles:

The next level of machine learning engineer jobs is Mid-Level, such as ML Engineer, MLOps Engineer, NLP Engineer, or Computer Vision Engineer. This role has more responsibilities, and here you have to perform mid-level complex tasks using your skills and experience.

Senior & leadership roles:

With more years of real projects, you can move to Senior ML Engineer, Lead ML Engineer, Principal Engineer, or even ML Engineering Manager. Here you’ll guide teams, design systems, and implement the ML roadmap.

Final Thoughts

Right now, jobs in machine learning and AI are some of the most active roles in tech. Companies in finance, health, retail, SaaS, and even small startups need people to implement working ML systems. According to the trends, more AI ML jobs will be available, from junior ML engineers to senior leaders. In 2025, the machine learning engineer jobs market is valued $113 billion(2025) and projected to exceed $500 billion by 2030.

A right machine learning course will help you to gain foundational skills such as working with data, developing models, writing clean code, and deploying simple projects. After course completion mention some good projects in your portfolio and start with entry-level roles. Then move step by step toward better pay and more responsibility.

The benefits of this job profile are clear: good salary potential, strong demand across countries, and flexible options like remote work. As an ML engineer, you will build the systems that actually change how products behave and how businesses make decisions, which will keep your job interesting over the long term.

So, are you ready to explore the machine learning engineer career path for you?

FAQs

What is a Machine Learning Engineer?

A machine learning engineer is an expert who develops the AI system that can learn from data and actions and make predictions.

What jobs can machine learning engineers do?

Machine learning engineer jobs are of different types, such as ML software engineer, ML scientist, Healthcare ML engineer, or others. So, based on the interest, a person can choose from categories like Core ML jobs, specialized roles, research-based, and industry-specific.

What is the role of a machine learning engineer?

The key role of an ML engineer is to bridge the gap between AI, data science, and software engineering by designing, developing, and deploying ML systems.

What skills does an ML engineer need?

An ML engineer needs solid Python skills, basic math and statistics, knowledge of ML algorithms, and the ability to work with data, models, and deployment tools in real projects.

What industries hire Machine Learning Engineers?

Machine learning engineers work in many areas: finance, healthcare, e-commerce, SaaS, telecom, manufacturing, gaming, marketing, and even robotics and autonomous driving.