Machine Learning Engineer Salaries

The notion of machine learning has been known for a long time, with Alan Turing’s Enigma machine from World War II serving as the first practical example. Currently, machine learning is employed in virtually every area of our lives, from simple everyday chores to more complicated computations involving large amounts of data. Google’s self-driving car, for example, is powered by machine learning, which also powers personalized recommendations on websites such as Netflix, Amazon, and Spotify. Because of the growing demand for machine learning, India has one of the highest compensation rates in the world.

Machine learning’s primary purpose is to assist businesses in improving their organizations’ overall functioning, efficiency, and decision-making processes by analyzing large volumes of data. As algorithms enable machines to learn, organisations will be able to identify patterns in data that will help them make better decisions without the need for human engagement. This will allow businesses to save time and money by eliminating the requirement for human interaction.

Who is a Machine Learning Engineer?

A Machine Learning Engineer is an avid programmer who helps machines understand and pick up knowledge as required. Their core deliverables include creating programs that enable machines to take specific actions without explicit directions.

Apart from programming, Machine Learning engineers are also responsible for customising data sets for analysis, personalising web experiences, identifying and predicting business requirements. This role also demands exceptional communication skills since they often collaborate with other teams to drive different optimisation projects. Hiring companies typically look for candidates with a master’s degree and a few years of experience in similar roles.

Following are a few of the Responsibilities of a Machine Learning Engineer

  • Creating Machine Learning programs using ML libraries
  • Experimenting with various machine learning programs to test their efficiency
  • Adapting Machine Learning programs for scalability
  • Maintaining data flow between database and backend systems
  • Debugging custom machine learning codes 
  • Optimising Machine Learning technologies in production environment

Machine Learning Engineer Job Description

We are looking for a Machine Learning Engineer to develop ML algorithms and make them production-ready. Our research focuses on human-ageing related diseases. The ideal candidate must have some background in research-oriented responsibilities to support us with relevant tools and programs. S/he will cross-function to create world-class machine learning platforms to advance our research effort. They will play a critical role in defining and executing optimisation strategies in computational biology and machine learning. Our biochemical formulas require candidates who can handle different types of biological data in huge volumes. 

Ideal candidates should be equipped with various data analysing techniques. They should have prior experience in implementing, extending, and debugging machine learning techniques. They should be able to design and build high-leverage data infrastructure and tools.

Responsibilities of a Machine Learning Engineer

  • Prototypes in data science should be studied and converted.
  • Machine Learning systems and schemes must be designed and developed.
  • Using test findings, undertake statistical analysis and fine-tune models.
  • To locate available datasets for training purposes on the internet.
  • To train and retrain machine learning systems and models as needed.
  • Extend and improve existing machine learning frameworks and libraries.
  • To create Machine Learning apps that meet the needs of customers and clients.
  • To investigate, test, and deploy appropriate machine learning algorithms and tools.
  • To assess machine learning algorithms’ problem-solving skills and applications and rate them according to their likelihood of success.
  • to investigate and observe

A sub-branch of Artificial Intelligence, Machine Learning demands a basic understanding of all the major AIML concepts. Machine Learning Engineers, in particular, are expected to be familiar with computer science, a little bit of data science, consumer trends and more. The following skillsets are, however, mandatory requirements to excel in the domain:

Machine Learning Engineer Skills

  1. Programming Language Knowledge: One of the foremost requirements of a career in Machine Learning is programming skills. There are different programming languages like Python, R, Java and C++ for different functions. While Python is the most commonly used machine learning language owing to its versatility and flexibility, other languages have their own benefits. For example, C++ is best suited to speed up your codes and R works better for statistics and plots. All these languages together help a machine language expert to understand data structures, memory management and class structure. 
  2. Probability and Statistics: Machine Learning engineers need to be adept in statistical concepts like Mean, Regression, Gaussian Distributions and Standard Deviations. Knowledge of probability theory is important for creating algorithms for Hidden Markov models, Gaussian Mixture Models, and Naive Bayes. These probability techniques help an ML engineer to handle the uncertainties of real-world challenges. Apart from distribution models, Statistical knowledge also equips ML engineers to work on analysis methods like hypothesis testing and ANOVA. In fact, a lot of the machine learning algorithms build on existing statistical models.
  3. Data Modeling & Evaluation: Data modelling helps Machine Learning professionals to create or estimate the structures of any given dataset. Essentially, data modelling allows data scientists and ML engineers to prepare the data set for any specific kind of analysis. This process helps in identifying patterns (clusters, correlations, etc), predicting properties (classification, anomaly detection, regression) and creating the analysis models accordingly. The data evaluation process further helps by choosing the best model to represent the data. Data evaluating can also help in estimating the success of any data model.
  4. Distributed Computing: Machine Learning experts often work with large data sets which involve using multiple machines. Knowledge of projects like Apache Hadoop and cloud services like Amazon EC2 comes handy in such situations to distribute it in clusters.
  5. Signal Processing Techniques: Feature extraction is a crucial part of machine learning. Hence it is important for ML professionals to know signal processing techniques to solve different problems. Apart from the advanced signal processing algorithm (Wavelets, Curvelets, Bandlets, Shearlets etc), time-frequency analysis also helps ML engineers in complex problem-solving.
  6. Computer Science Fundamentals: Computer Science fundamentals like computer architecture, data structure, computability and complexity are important for a machine learning engineer to implement or modify programs according to requirements.
  7. Machine Learning Algorithms and Libraries: Even though ML libraries and packages are freely available with algorithms, not all of them are suited for all kinds of applications. ML engineers should know how to apply them effectively to optimise the outcome. Choosing the right data model, algorithm, and learning procedure is as important as knowing the libraries or languages. ML engineers should be able to discern the advantages and disadvantages of any particular algorithm and when to use them. 

Planning to build a career in Machine Learning and wish to check your salary growth in 5 & 10 years, and compare your current salary v/s peers? Check out Great Learning’s Salary Builder and get powerful insights to grow your career.

Machine Learning Salary based on Experience

Experience Level Salary
Beginner (1-2 years) ₹ 5,02,000 PA
Mid-Senior (5-8 years) ₹ 6,81,000 PA
Expert (10-15 years) ₹ 20,00,000 PA
Machine Learning Salary based on Experience

Machine Learning Salary based on Job Title

Job Title Salary
Artificial Intelligence Researcher ₹ 9,00,000 PA
Machine Learning Engineer ₹ 9,29,923 PA
Machine Learning Salary based on Job Title

Machine Learning Salary based on Company

Company Size
Deloitte  ₹ 6,51,000 PA
Amazon ₹ 8,26,000 PA
Accenture ₹15,40,000 PA
Machine Learning Salary based on Company

Machine Learning Salary in Other Countries

Here’s the list of salaries of Machine Learning Engineer in other countries:

Country Salary
US $140,675
Canada $93,684
Australia $106,532
Machine Learning Salary in Other Countries

In the US, the top companies hiring for this role are eBay, Wish, etc. The cities with the highest salaries are San Francisco Bay Area, Cupertino, and Santa Clara, etc.

In Canada, the top companies hiring for this role are OCAD University, Workday, etc. The cities with the highest salaries are Waterloo, Vancouver, etc.

In Australia, the top company hiring for this role is CSIRO. The cities with the highest salaries are Sydney, Melbourne, Perth, etc.

Machine Learning Salary in India based on Skills

Skills  Average Salary
Machine Learning 7 Lakhs Per Annum
Natural Language Processing 7.3 Lakhs Per Annum
Artificial Intelligence 8 Lakhs Per Annum
Deep Learning 7.5 Lakhs Per Annum
Computer Vision 7.25 Lakhs Per Annum
Machine Learning Salary in India based on Skills

A Day in the Life of a Machine Learning Engineer

Machine Learning engineers usually spend a lot of time programming but before they get into that they start their day by catching up on their emails. Pretty basic right?

You might think that ML engineers function like the rest of us, going through the day managing various routine work. However, you’d be surprised to know that Machine Learning engineers need to work on a lot of interdisciplinary tasks, ranging from data science, analytics, business communication and more. We have tried to put all the tasks together that a machine learning engineer engages in on a typical day.

  • Check the models that have been active for a while 
  • Connect with the rest of the team for updates 
  • Look through task management platforms to schedule the day
  • Analyse company codebase using Scikit learn to look for bugs
  • Code with PyCharm to implement a model or keep the interfaces of a database running
  • Meet stakeholders to ensure products are updated with new features and changes are implemented according to plans
  • Discuss how to optimise products and create plans and processes for it
  • Research on the latest trends in the domain and how the company can benefit from it

What are The Advantages of a Machine Learning Course?

1. More opportunities for advancement and advancement in your career

According to TMR, MLaaS (Machine Learning as a Service) is expected to rise from $1.07 billion in 2016 to $19.9 billion by the end of 2025. This is an astounding level of increase, both in terms of raw numbers and year-over-year comparisons.

Machine learning makes a mockery of anything that can be described as “important” on a financial or global scale. If you want to push your profession to the next level, Machine Learning can help you achieve it. Machine Learning can also help you get involved in something that is both global and relevant today.

2. Increased Salaries

The greatest machine learning engineers nowadays are paid as much as really well-known athletes! That is not an exaggeration! The average machine learning engineer income is 8 lakhs per year, according to – and that’s only at the beginning of one’s career! A skilled machine learning expert might earn anywhere between 15 to 23 lakhs per year.

2. Salary Increases

Today’s top machine learning engineers are paid on par with world-famous athletes! That is not hyperbole! According to, the typical machine learning engineer earns Rs. 8 lakhs per year – and that’s only at the start of their careers! A proficient machine learning expert can expect to earn between 15 and 23 lakhs per year.

3. Corporations are afflicted by a scarcity of machine learning skills.

Given the rapid rate of technological advancements, many businesses have been forced to play catch-up. The truth is that there are simply not enough machine learning professionals to meet new industry expectations in the digital transformation business.

4. Data science and machine learning are inextricably connected.

Because of its all-explaining nature, as well as its financial and inventive viability, Data Science currently rules the people in the same way that religion governed the people for millennia before modernity.

And Data Science is only a phantom of Machine Learning in terms of functionality. The ability to become adept in each of these areas will allow you to analyze a horrifying amount of data and then extract value and give insight from it, which will propel your career to new heights.

Furthermore, because ML engineers and Data Scientists frequently collaborate on products in many organisations, if you’ve already worked as an ML engineer, you may find yourself exposed to the Data Scientists’ point of view as a result of your previous work.

How to Become a Machine Learning Engineer

Machine Learning has established itself as a promising domain for professionals who want to make a difference in the fast-changing digital economy. Upskilling in this field will land you lucrative offers from international brands. Great Learning’s PGP-Machine Learning offers a comprehensive course structure that prepares candidates with industry insights to meet real-world challenges.

Research and find job openings that meet your skillset and then apply for them. Here’s a list of articles that will help you understand the fundamental concepts of Machine Learning and prepare you for the interview:

Your job doesn’t end at nailing the interview. You must keep yourself updated on Machine Learning trends and company goals to grow in the role.

Machine Learning Career Path – How to Grow in ML Roles

Machine Learning Career Path


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