Machine Learning Engineer Job Description

Machine Learning Engineer Job Description, Skills, and Salary

Get to know about the duties, responsibilities, qualifications, and skills requirements of a machine learning engineer. Feel free to use our machine learning engineer job description template to produce your own. We also provide you with information about the salary you can earn as a machine learning engineer.

 

Who is a Machine Learning Engineer?

Machine learning engineering is the process of employing software engineering concepts, analytical and data science skills, and combining both of those to take an ML model that has been generated and make it available for usage by the product or the users. Machine learning (ML) is a topic of study focused on comprehending and developing “learning” methods, or methods that use data to enhance performance on a certain set of tasks. It is regarded as a component of artificial intelligence. To generate predictions or choices without being explicitly taught to do so, machine learning algorithms develop a model based on sample data, often known as training data.

Machine learning algorithms are utilized in a wide range of applications, including computer vision, email filtering, speech recognition, and medicine, where it is difficult or impractical to create traditional algorithms to carry out the required tasks. Not all machine learning is statistical learning, although a portion of it is strongly related to computational statistics, which is centred on computer prediction. The discipline of machine learning benefits from the methodology, theory, and application domains that mathematical optimization research provides. A similar area of study is data mining, which focuses on unsupervised learning for exploratory data analysis. Some machine learning applications use data and neural networks to simulate the operation of a biological brain. Machine learning is also referred to as predictive analytics when used to solve a variety of business problems. To help create high-performance machine learning models, machine learning engineers must evaluate, organize, and analyze data as well as run tests and optimize the learning process.

A machine learning engineer is more of a tech expert that creates, updates and maintains AI systems that support models. They take algorithms developed by data scientists and scale them to work in a production environment or organizational process, in other words. MLEs make sure the models are delivered to your phones, PCs, and other electronic devices. Highly trained programmers known as machine learning engineers create artificial intelligence (AI) systems that leverage massive data sets for research, development, and generation of learning and prediction-making algorithms. To automate predictive models, machine learning engineers conduct research, create, and design self-running software.

A machine learning (ML) engineer creates artificial intelligence (AI) systems that use massive data sets to produce and construct algorithms capable of learning and eventually generating predictions. You must learn how to program if you want to work in the field of artificial intelligence and machine learning. A programmer needs to be familiar with a variety of languages, including C++, Java, and Python. However, not all successful machine learning engineers need to be experts in HTML or JavaScript. Machine learning is by nature an iterative process. To be effective in this role, one needs to enjoy that style of development. Building a machine learning system requires one to build a very simple model quickly at first, then iterates on getting it better with each iteration. Languages like R, Lisp, and Prolog have also become important languages for machine learning.

The majority of machine learning engineers have backgrounds in computer science, engineering, mathematics, or data science, however, they can come from any subject. While having a strong foundation in math and computer science is essential, many people are choosing to pursue certification courses, many of which can be completed online, to gain the additional skills and knowledge required to become a Machine Learning Engineer. These knowledge areas include understanding supervised and unsupervised learning, deep learning, regression, classification, clustering methods, and neural networks. An ML Engineer would most likely be a member of a bigger data science team, depending on the size of a business. The members of that team could be data scientists, analysts, engineers, architects, and database administrators.

Depending on their degree of authority, Machine Learning Engineers could potentially engage with a wide range of diverse stakeholders within an organization, including everyone from top business leaders to marketing, sales, IT, software development, or web development teams. The study of machine learning is still somewhat young. Numerous approaches, instruments, algorithms, and applications still need to be developed. ML engineers must prioritize learning by nature, much like software developers do. It’s also crucial to use courses, blogs, tutorials, and podcasts to stay current in a sector that is still developing and changing quickly. You should exhibit strong data science expertise and relevant experience in a similar ML function to succeed as a machine learning engineer. Someone with first-class machine learning engineering skills will be able to improve the performance of predictive automation software.

 

Machine Learning Engineer Job Description

Below are the machine learning engineer job description examples you can use to develop your resume or write a machine learning engineer job description for your employee. Employers can also use it to sieve out job seekers when choosing candidates for interviews.

  • Comprehend and put into practice computer science concepts including data structures, algorithms, computability and complexity, and computer architecture.
  • Master and apply the foundational concepts of computer science, such as data structures, algorithms, computability and complexity, and computer architecture.
  • Utilize extraordinary mathematical abilities to carry out computations and interact with the algorithms used in this kind of programming.
  • Create project outcomes and identify the problems that must be fixed to improve the effectiveness of programs.
  • Build data and model pipelines in collaboration with data engineers.
  • Control the data pipelines and infrastructure required to deploy code.
  • Demonstrate a complete understanding of the applications being developed, including but not limited to the machine learning algorithms.
  • Construct algorithms based on statistical modelling techniques, and construct and maintain scalable machine learning systems in use.
  • Communicate with people who are not programmers and give them detailed explanations of complex operations.
  • Study and use best practices to enhance the current machine learning infrastructure.
  • Give engineers and product managers assistance with integrating machine learning into the product.
  • Develop project results and identify problems that need to be fixed to make programs more effective.
  • Create data and model pipelines with the help of data engineers, and oversee the infrastructure and pipelines needed to get code into production.
  • Demonstrate your mastery of the entire application development process (including, but not limited to, machine learning methods)
  • Use algorithms based on statistical modelling techniques to create and sustain scalable machine learning systems in production.
  • Discover trends and predict previously undiscovered events by using data modelling and assessment techniques.
  • Utilize libraries and algorithms for machine learning
  • Lead in the development and engineering of software
  • Convey and clarify challenging techniques to non-programming experts
  • Engage stakeholders in conversation to analyze company problems, articulate needs, and assess the scope of the solutions that are needed.
  • Analyze large, complex datasets to uncover insights and select the optimal strategy.
  • Investigate and put best practices to use to improve the machine learning infrastructure that is already in place.
  • Help the product managers and engineers incorporate machine learning into their designs.

 

Qualifications

  • A bachelor’s degree in mathematics, computer science, data science, or a similar subject.
  • A master’s degree in data analytics, computational linguistics, or a related field would be helpful.
  • A machine learning engineer with at least two years of experience.
  • code writing in Python, Java, and R to an advanced level is required.
  • Extensive understanding of software design, data modelling, and data structures.
  • Working knowledge of ML frameworks.
  • Good software engineering skills.
  • Knowledge of data science.
  • Working knowledge of ML libraries and packages.
  • Understanding of computer architecture
  • Inventiveness and innovation.
  • Ability to work with massive, complicated datasets.
  • Comprehensive understanding of mathematics, statistics, and algorithms.
  • Outstanding analytical and problem-solving skills.
  • Excellent interpersonal and teamwork abilities.
  • Excellent organizational and time management skills.

 

Essential Skills

  • Communication Skills: Communication skills are crucial for machine learning engineers because they frequently collaborate with other team members. They frequently communicate with the other team members to exchange ideas, go through tasks, and respond to inquiries. Additionally, they speak with clients to discuss their work and respond to inquiries regarding their projects. Your professional trajectory may change drastically if you have strong communication abilities. This is because, even though you are the best at comprehending the data and insights produced by machine learning, it is also crucial that you can communicate these insights to non-technical team members, shareholders, or clients. This may also entail employing data storytelling, in which case you must be able to present your data narratively with a beginning and an end at specific outcomes you have discovered by applying machine learning to the data. This is so that a firm may focus more on the actionable insights that can be drawn from the data, which are ultimately more essential to a company than the data analysis. And if you can successfully communicate these thoughts, the sky is the limit for you in your line of work.
  • Data Modeling and Evaluation Skills: Data modelling and evaluation skills are essential for machine learning engineers. Finding patterns that are hidden from the naked eye requires first understanding the underlying structure of the data. The data must also be evaluated using an algorithm that is appropriate for the data. For instance, the data will determine which machine learning algorithms, such as regression, classification, clustering, dimension reduction, etc., to apply. A regression approach for accuracy might be a random forest, whereas a classification algorithm for vast data and speed might be naive eyes. Similar to how the probability clustering procedure is k means and the k mode for categorical variables. To properly contribute to data modelling and evaluation, you need to be aware of all these specifics of different techniques.

 

  • Mathematics and Statistics knowledge: Without at least a basic understanding of math, machine learning is impossible to grasp. Whether you have formal training in math and statistics or not, you’ll need to have math skills equivalent to those found in high school to keep up. A formal concept of probability and methods developed from it are at the core of many machine learning algorithms. The study of statistics, which offers many measures, distributions, and analysis techniques required for creating and testing models from observable data, is closely related to this. Many machine learning methods are just extensions of statistical modelling techniques. Your ability to evaluate data and use it in practical applications will be aided by your statistical skills. This can assist you in creating powerful machine learning models and deciphering your research’s findings. To increase your knowledge, think about reading books or taking a statistics course.
  • Computer Science Fundamentals and Programming Skills: This is another prerequisite for developing into a competent machine learning engineer. Different computer science concepts, such as data structures (stack, queue, tree, and graph), algorithms (searching, sorting, dynamic and greedy programming), space and time complexity, etc., must be familiar to you. The good news is that if you have a bachelor’s degree in computer science, you presumably already know all of this! You should be knowledgeable in a variety of programming languages, including Python and R for machine learning and statistics, Spark and Hadoop for distributed computing, SQL for managing databases, Apache Kafka for pre-processing data, etc. It’s great if you are familiar with Python’s libraries, such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc. Python is a very popular programming language, especially for Machine Learning and Data Science.

 

  • Machine Learning Algorithms Knowledge: Knowing every common machine learning algorithm is crucial for knowing when to use which algorithms. Most machine learning (ML) algorithms fall into one of three categories: supervised, unsupervised, or reinforcement learning algorithms. For example, Naive Bayes Classifier, K Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc. are some of the more popular ones. Therefore, it’s beneficial if you have a thorough understanding of each of these algorithms before starting your career as an ML developer.
  • Neural Networks Understanding: The role of neural networks in machine learning cannot be taken for granted. These networks, which are modelled after neurons in the human brain, have multiple layers, including an input layer that receives data from the outside world and multiple hidden layers that transform it into information useful for the output layer. These networks show a keen understanding of parallel and sequential computations that are used to analyze or learn from data. There are numerous varieties of neural networks, including Feedforward Neural Networks, Radial Basis Function Neural Networks, Recurrent Neural Networks, Convolutional Neural Networks, and Modular Neural Networks. Being an ML engineer does not require you to fully comprehend all of these neural networks, but you must be familiar with the foundations.

 

How to Become a Machine Learning Engineer

Step 1: Undergraduate Degree

An aspirant machine learning engineer should ideally have an undergraduate degree in one of those fields since math, data science, computer science, and programming are the main subject areas needed for this profession. It is also possible to apply for degrees in related disciplines like physics or statistics. Business degrees can also be a good starting point for machine learning engineers because they require a strong business sense to understand the data requirements of employers. However, these degrees must be supplemented with extensive technical training in the required sciences.

Step 2: Training and Experience

Engineers in machine learning frequently learn on the job from their current or prior employers. Junior machine learning engineers might learn the unique tools and methods that the organization uses through this training. They can also pick up knowledge from more experienced coworkers. Professionals will not only be able to at least get their foot in the door as machine learning engineers but will also have more possibilities thanks to more training and experience. Those with experience, education, and, of course, good leadership qualities will be eligible for management leadership jobs. Additionally, a lot of research is being done on AI and machine learning, mostly by major tech firms like Apple, Google, and Microsoft. Future developments in machine learning may very well be influenced by these research perspectives. Freelancing is a lucrative and adaptable professional career option since some companies that can’t afford a full-time machine learning staff hire independent machine learning experts to develop and install specific ML systems. Additionally, professor positions at universities will undoubtedly be available for people who want to instruct the upcoming generation of machine learning engineers.

Step 3: Master’s or doctoral degree

For the great majority of machine learning engineer job vacancies, an undergraduate degree will not be sufficient. Machine learning engineers would have a wide range of alternatives thanks to master’s degrees in data science, computer science, software engineering, or related fields, or even a Ph.D. in machine learning.

Step 4: Machine Learning Portfolio

The lack of expertise working on a variety of machine learning projects is the weakest aspect of the majority of machine learning resumes. If this is your resume, concentrate on creating a killer portfolio by including some intriguing machine learning work. Every ML developer should have an online resume that demonstrates their aptitude for using machine learning to solve complex challenges. A machine learning portfolio should ideally include any interesting ML projects you’ve worked on as a freelancer or other opportunities. Particularly for those just entering the field, you’ll need to create a machine learning portfolio that will help you land a job as a machine learning engineer. ProjectPro, which serves itself as “the one-stop platform to do data science and machine learning projects,” is one way to go about it. If you’re just starting to apply for a machine learning engineering position include varieties of projects in your portfolio that demonstrate expertise in machine learning concepts like NLP, Neural Networks, Distributed Computing, Data Modelling and Evaluation, and Reinforcement Learning, along with practical experience with tools and technologies like Python, R, TensorFlow, Keras, etc. All engaging machine learning projects—whether done for hiring managers or to gain experience—count.

 

Where to Work as a Machine Learning Engineer

Machine learning engineers work in the artificial intelligence field. To design programs that enable machines to operate without direct human aid, they work in collaboration with data scientists, computer engineers, and developers. Although they may travel to conferences or client meetings, machine learning engineers typically operate in offices. Although they occasionally put in extra effort to meet deadlines or attend activities on the weekends or after hours, they generally work conventional business hours. They frequently work in teams with other engineers and computer scientists and may cooperate with other professionals, such as marketing experts or salespeople, to design and implement machine learning solutions. Although the necessity to meet deadlines and the pressure to generate results may cause some stress for machine learning engineers, they typically find their work to be challenging and rewarding.

 

Machine Learning Engineer Salary Scale

In the US, a machine learning engineer makes an average pay of $110,909 per year. The average salary for a machine learning engineer in the UK is £52,000. As a graduate beginning your career, you can expect a salary of around £35,000. The salary of a machine learning engineer can increase to as much as £170,000 if you work for a large multinational company like Google or Facebook.

In Nigeria, a person working as a machine learning engineer typically earns around 381,000 NGN per month, with a range falling from 190,000 NGN to 590,000 NGN. The pay for contract work is about £450 per day, and benefits can include a company pension plan, private health insurance, and discretionary bonuses. Salaries vary and are based on experience, specialization, and qualifications.

 

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