A Machine Learning Engineer’s Career Path and Salary in the USA

As data science and related technologies advance, so does the need for skilled machine learning engineers. More and more businesses are deciding to hire machine learning experts to better their products and services. It is essential to collaborate with technology to expand and provide a unique service. Due to the exclusive nature of this field, machine learning engineers enjoy a high salary. There’s a good explanation for it, too. It calls for a high level of specialized education, a well-developed skill set, and a natural aptitude in the scientific realm.

Who are Machine Learning Engineers, and what is their role?

The field of data science encompasses a lot more than just one area. It makes use of programming but places more emphasis on an analytical method of approaching data. As such, a data scientist’s job entails sifting through information to find insights that may be applied to a company’s operations. A data science team may include an engineer or specialist in machine learning. Modeling is at the heart of ML, and the models created can be put to any number of uses down the line, including but not limited to product enhancement.

 Engineers skilled in machine learning may design an image recognition system that can distinguish between different sorts of trash based on images, estimate future energy requirements, anticipate product sales based on past trends, and even foresee the spread of an epidemic.

The ML engineer is a math and computer whiz who also has a firm grasp on the fundamentals of programming, probability, and statistics. As such, not everyone will have the ability to learn and use them.

How much does a Machine Learning Engineer earn?

The salary of a machine learning engineer can be affected by factors such as the company, the position, and the location. However, the most significant factor is years of practice. The number of zeros in salary is defined by one’s experience and expertise in data science. This is because an employee’s worth to the organization directly correlates to their level of knowledge. They are compensated according to the value they create.

Per the research done by Payscale, Machine Learning Engineers, on average, make $112,452 annually.

  • Entry level – A Machine Learning Engineer with less than a year of experience earns an average salary of $93,867 (this does not include bonuses or other forms of compensation).
  • Early Career level – The typical salary for a Machine Learning Engineer with 1-4 years of experience is $111,870.
  • Mid-Career Level – With 5-7 years of experience, a Machine Learning Engineer earns an average salary of $141,720 or above.

Career Path

Machine learning’s popularity stems from the fact that it allows computers to learn independently, which cuts down on human involvement and boosts their efficiency. As a result, Machine Learning offers a wide variety of in-demand and lucrative careers, such as Machine Learning Engineer, Data Scientist, Natural Language Processing (NLP) Scientist, etc.

Many Machine Learning Engineers popularly move from Machine learning engineers to Data Scientists as it gives an individual more career-level opportunities in the future. After gaining a good experience of 5-8 years, machine learning engineers can also work as Senior machine learning engineers or Data Scientists based on their interests. After working as a Data Scientist and gaining good work experience, an individual gets to work as Senior Data Scientist, Data Science Manager, and Data Science Director as per the increasing experience level. Other career opportunities are NLP Scientist (where NLP stands for National Language Programming), Business Intelligence Developer, and Human-Centred Machine Learning Designer.

What are the skills required for the Machine learning engineer role?

Professionals with expertise in Machine Learning, Natural Language Processing (NLP), and Deep Learning earn more than their peers. A company’s or engineer’s concentration on a particular industry may dictate the required skills. What matters most, though, is what you do with it and how open you are to change and improvement. The most popular skills for machine learning engineers are Data Analysis, Data Modeling, Deep Learning, Natural Language Processing (NLP), R Programming Language, Python, Scala, Artificial Intelligence (AI), Reinforcement Learning, and Image Processing.

Cities and companies paying the most

Engineers specializing in machine learning in San Francisco, California, make more significant money than their counterparts elsewhere in the United States. Salary levels in these fields are above the national median in New York City and San Francisco, California. Cities like Santa Clara, Dallas, Austin, Bellevue, and Coldwater also pay the highest salaries compared to other cities.

The top companies paying excellent salaries to Machine Learning engineers in the United States are Bayer, IBM, Ford Motor Company, eBay, Capital One, Tapjoy, Snap, General Assembly, Twitter, Airbnb, DoorDash, and DispatchHealth.

As businesses put in more effort to research and build cutting-edge goods, machine learning experts might see a rise in demand in the coming years. Additionally, investing in AI and other forms of tech that aid in moving forward is a great strategy to expand the business and career.

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References:

  • https://www.payscale.com/research/US/Job=Machine_Learning_Engineer/Salary
  • https://builtin.com/salaries/dev-engineer/machine-learning-engineer
  • https://www.indeed.com/career/machine-learning-engineer/salaries
  • https://www.payscale.com/research/US/Job=Machine_Learning_Engineer/Salary
  • https://www.geeksforgeeks.org/top-career-paths-in-machine-learning/

Dhanshree Shenwai is a Consulting Content Writer at MarktechPost. She is a Computer Science Engineer and working as a Delivery Manager in leading global bank. She has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world.