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Cost to Hire Machine Learning Developers

Here Mobilunity is going to explain some basic industry-related terms, discuss what industry adopted (or plan on adopting) the technology, answer the questions “how much does it cost to hire machine learning developer” and “how much does it cost to hire a programmer”, and explore machine learning and artificial intelligence developer wage. In determining the cost to hire machine learning developers, it’s important to consider additional resources, for example hire remote backend developer, collaborating with an SAP Crystal Reports developer, and engaging a freelance software C + developer to create a well-rounded team capable of tackling complex projects. Additionally, understanding the costs and benefits to hire data scientist developers is crucial for businesses aiming to leverage data-driven decision-making and advanced analytics techniques.

Artificial Intelligence vs Machine Learning

What Is AI (Artificial Intelligence)?

Some people who studied literature are thinking about Talos, others think about Terminator, but let’s get more serious. The concept is not new, a theoretical base was developed in the 30th years of the 20th century. The basic idea came from biology and math, but we will try to keep it simple. The biology part is the basic layout which consists of neuron and perceptron. Data is passed to perceptron and there translated to a number. The neuron has the formula which is applied to the number, and the number is used to make a decision. For the math part, a neuron is a function, which processes the part of the data, and results are like rating and is passed through layers of neurons. This is a simplified explanation, but it’s not a new technology, neural networks have been used in photo cameras for years. If you’re planning to incorporate artificial intelligence into your business, it’s crucial to understand the cost associated to hire AI developers to bring your vision to life.

What Is Machine Learning?

Machine learning is the way to reach artificial intelligence. The term artificial intelligence means that the machine can show intelligence, which means that it can learn. This learning process differs from humans’, but the basic idea is similar. Machine learning is the way to teach the machine and gain experience which can be adopted later or be loaded to another machine. There are two phases of machine learning. First, is to write the algorithm itself and the second one is to teach it, (with a ‘teacher’ which will point the correct answer). Generally thousands, it takes thousands of runs after which the algorithm is starting to show at least 80% of accurate answers.

Machine learning is basically creating algorithms and software that is user experience. The experience or, as it is called, ‘data model’ can be saved to some data storage (sort of an advanced case of content-driven development). Based on this model, the same algorithm can be adapted to be used in different runtimes or even for different tasks. Writing the algorithm is complex and requires a mathematical background since all the incoming data (images, text data, sound) should be processed to numbers or like numbers structures called tensors and then passed to neural networks, where it can be processed. Also, the amount of data that should be used to get reliable results must be huge, in some cases, it can include petabytes of information.

machine learning tools

What is the difference between machine learning and artificial intelligence?! Basically, these are almost the same, AI is a more advanced usage of neural networks. Machine learning is a form of artificial intelligence that learns patterns in data and uses them to predict patterns in new data.
When evaluating the cost to hire machine learning developers, consider the potential benefits of working with a freelance Objective C developer and exploring software outsourcing Bangladesh, which can offer cost-effective solutions and diverse expertise for your project.

Why ML and Machine Learning Developer for Hire Are Popular Nowadays?

Basically, the algorithm can learn, and the calculated numbers can be saved as experience. It means that once the algorithm has enough experience, it can be scaled for much bigger and complex tasks. About ten years ago, computers became able to handle big chunks of data and do complicated calculations, so neural networks can be used for more elaborate tasks. It’s used now for classification of data, e.g. computer vision. Apple Face Recognition, even the advanced cases of cameras that can find a person (or group of people) and even detect the identity (like popular social media that learns to find you in photos where you haven’t even been tagged), or count the people on the video stream ー all this presents infinite opportunities in the future.

Practical Application of AI and ML in Business

Machine learning is one form of technology that allows organizations to make better decisions without human intervention. When used correctly, machine learning can help businesses become more efficient. It allows businesses to analyze bigger and more complex data and deliver faster, more accurate results.

How Can a Business Entity Benefit from AI and ML?

AI and ML solutions are popular now, and, frankly speaking, the buzz won’t go away for a long time. Google Cloud reports that among CIOs of medium-to-big companies 21% already have pilot projects that involve AI and machine learning within their domain, 25% will adopt it on medium-to-long-term projects, and 85% will have pilot or pet projects by 2020. Among the ‘traditional’ applications (or at least those that already have proved its efficiency and have real-life cases) are:

  • Cheaper analytics. Human resources can cost a fortune when it comes to data analysis. Huge amounts of information can take months for human beings to analyze, process, and spot a trend (when its already gone). AI can make that much faster and spot tendencies and patterns where the human brain could not.
  • Hiring. No, we are not offering Sofia the robot to conduct the interview. However, artificial intelligence can save time browsing through numerous CVs and applications, sorting each SW engineer’s application that better matches the set parameters.
  • Technical customization. As much as we all love autofill options, AI can speed up interactions with websites figuring out your name/e-mail and improving the overall experience.
  • Anticipation. We are not talking about predictions about the future, but rather first-hand information about customer’s experience with the product, ways they feel about it, or potential risk of client loss.
  • Security. When it comes to security, there is no ‘too much’. Apart from being able to learn what a customer wants, AI can also spot someone who just pretends to be one and use technology like generative adversarial networks react to attacks in real-time (based on the analysis of situation).

Industries That Utilize AI and ML the Most

Industries of all types are using machine learning technology to operate more efficiently and stay ahead of their competition. Some examples of industries that have already unveiled the value of machine learning are financial services, health care, oil and gas, government, marketing and sales, and transportation. While machine learning has been around for a long time, today’s version is much different than the machine learning we knew of years ago. Machine learning in finance, healthcare, government, marketing will change the world. According to the LinkedIn Emerging Jobs Report, AI skill has penetrated numerous industries and shows a significant growth in demand over a period of 3 years.

ai skills penetration across industries

Among the top industries that require the immediate assistance of highly-capable specialists and have open job opportunities are:

  1. Computer Software
  2. Information Technology & Services
  3. Internet
  4. Research
  5. Financial Services
  6. Higher Education
  7. Automotive
  8. Telecommunications
  9. Consumer Electronics
  10.  Semiconductors

Among the use cases that are explored right now are computer vision, data classification, prediction, etc. AI is used for financial predictions and detecting risks since it’s based on big amounts of data (mostly transaction statistics). It’s used in healthcare to classify patients, to find co-dependency between physical parameters and illnesses, or to flag critical conditions before they happen. And let’s not forget about the military industry that uses AI to make weapon smarter and more reliable. A big is is the IoT, would it be great that the house can turn on the alarm or control the settings without of reconfiguring the parmas in some application, or it can detect weather conditions just pointing a camera to the sky and seal windows or put the roof over the terrace. The main advantage of using AI is that software can be adapted to different runtime environments without actually writing code. According to Moore’s law, in the future, it will be a lot easier to provide resources for AI software. Also, it will give a lot of workplaces for AI engineers.

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Why Companies Hire Machine Learning Developers and AI Engineers?

As for February 2019, Indeed.com has listed more than 4,000 positions for AI Engineers, and almost 16,5 thousand job opportunities for machine learning engineers. According to the LinkedIn Emerging Jobs Report, artificial intelligence and machine learning experts are reaching striking demand. Machine learning engineers are in demand right now, and since 2014 the interest to and need in such experts has grown 12 times. Among the skills that are required most are: deep learning, machine learning, natural language processing, Tensorflow, Apache Spark. Another interesting discovery is that machine learning specialists (a broader term that refers to experts of the industry) have 6-times growth over the same period of time. Dominating skills include Python, artificial intelligence, and deep learning.

Must-Have Skills in AI/ML Programmer Resume

Before we discuss AI developer CV, let’s focus on what skills are needed and how they correlate with this technology? There is a great explanation by Drew Conway, which seems to be the perfect explanation for how data science differs from machine learning and what do they have in common. However, even more questions emerge: where to begin? What specific skills does one need? What experience should a SW engineer have?

Where to Look for a Reasonable Machine Learning Engineer Cost and Why to Hire ML Developers?

The answer is simple, people and hardware. Two kinds of developers are needed: data scientists and developers for building infrastructure. Data scientists are the AI developers who provide the core of the algorithm, and the processing of raw incoming data, as well as putting it into the neural network. AI developer should have a math-related degree since most of the data science frameworks require a good knowledge of it. Most of them use Python for development since it’s simple, and numerous frameworks like Pandas or Keras are written in Python. The headliner of Machine learning is Tensorflow which is developed by Google. One of the requirements for Tensorflow is hardware since it’s can be executed using CUDA (running code using GPU), for production purposes a solution like NVIDIA DGX2 can be used. Such hardware cost a lot, but the data can be processed faster compared to the traditional datacenter machines that are using GPU.

Widely used machine learning platforms include Apache MXNet, TensorFlow, Caffe2, CNTK, SciKit-Learn, and Keras (and the overall machine learning engineer salary will be based on both their knowledge and experience). Other developers/engineers, involved in the process, provide infrastructure to deploy huge amounts of data for ML solutions. Usually, the data providers are arranged as cloud solutions using AWS, Azure, DigitalOcean, etc. To reduce the cost of the solution runtime, it is good to have a DevOps, or for example Kubernetes application developer, in case you have an app built with Kubernetes that can optimize it to use fewer resources and to make it efficient. But even here Python experience is preferable since all the integration is made with Python-based software.

A good resume of AI developer should have about 4-5 years of experience for senior staff. If more years are listed in AI programmer resume, that’s probably not true, since earlier neural networks were used only in universities. They should be solid experience with Python frameworks and cloud platforms, and UNIX-based systems (since windows don’t have CUDA support). Another part of the CV should include more specific information whether this the data scientist or infrastructure developer (they will have different backgrounds).

List of Skills Required for a Machine Learning Engineer

  1. Exquisite math skills, in order to work with algorithms;
  2. Knowledge of Python, R, Java, and C++ (depending on the type of a project/domain);
  3. Hands-on experience with typed language (e.g. C++ and Java)
  4. Big data experience or knowledge: data preprocessing, SQL & NoSQL, ETL (extract, transform, load), data analysis, and visualization.
  5. Understanding of rapid prototyping;
  6. Based on the needs of a project, understanding concepts like Auto-ML, voice and audio processing, language processing or robotic vision might be required.

How to Hire Machine Learning Engineer?

Hiring a dedicated machine learning developer is a complex task with several stages. Usually, the hiring team consists of a Technical Recruiter, a Team Lead, or another specialist with deep knowledge of the sphere. Some companies prefer to ask candidates to complete a test task. That’s a good way to check engineers’ skills and find a high-qualified professional. Others have another strategy. They invite candidates to a technical interview and give them real-time tasks to see the way candidates think and how they solve extraordinary problems. If you need to hire machine learning engineers with strong technical skills and vast experience, check the 5 questions below.

5 Main Interview Questions to Hire Machine Learning Engineer

  • Name the main ML approaches

Whether your goal is to hire a machine learning mobile app developer or to hire machine learning web developers, this question is a basis. The candidates must know that there are the main methods in data science and realize the differences between them. So, the good candidate can explain these approaches and note in which case each of them could be applied. They are supervised machine learning, unsupervised machine learning, and reinforcement learning. In some cases, semi-supervised also can be used. Give the candidate an ML task and ask them to say what approach would be the best to deal with this task. The same problem could be solved in a few ways, so be aware of it.

  • List the AI and ML tools you have experience with

An engineer who understands fundamental ML principles can quickly learn the required tools. But to hire neural network developers for a specific project, you need to check the tools they are familiar with. Pay attention not only to the theoretical knowledge of the programs for data science. Ask about practical experience and the results they got while using mentioned ML tools. You can also ask about links to their GitHub account to check some code examples. If you need to hire an engineer for a big-data project, clarify if they have already worked with big-data tools and analytics. This area requires some special skills.

  • Explain the lifecycle of an ML project

You can use this simple question in the first stage of recruiting funnel when the recruiter has a short call with the candidate. All ML engineers, even Juniors, know the answer. The knowledge portal for data science specialists, Analytics Vidhya, defines 5 major stages of ML projects. They are:

  • Distinguishing the business needs
  • Gathering relevant data
  • Data preparation
  • Data modeling
  • Feature engineering
  • Model building and deployment
  • What was the last book you read in the field?

To hire machine learning app developer, you need to find a specialist with a constant desire for self-education. Data science is a fast-changing area, and AI or ML concepts are updated constantly. So, ML professionals must be aware of the recent trends, researchers, and tools. That’s why asking candidates about professional forums, resources, or books they read is a good idea. You can also ask them about a brief summary or insights from the book. Here you can check the most popular AI and ML books on Amazon.

  • Name the main ML problems and how to overcome them 

The last interview question relates to ML issues. You can ask candidates about the most difficult problem in their careers or about a case they are proud of. You can also provide examples of the common ML problems and see the reaction of the specialist. For example, ask about inadequate infrastructure, lack of quality data, or issues with ML model implementation. Check the candidates’ creativity and the solving-problems approach. 
These main technical questions will help you hire dedicated machine learning developer who meets your technical requirements. Don’t forget questions to check soft skills to find a good team player for your project.

Overview of Machine Learning Developer Cost

It is a common belief that developing software based on ML algorithms and AI technology costs a fortune. A couple of years ago these technologies were available only to such giants as Google and Microsoft. But nowadays, companies can utilize AI/ML-based frameworks, libraries, and other tools to build new-level software. Those businesses who were wondering how much does artificial intelligence cost some time ago, now utilize these technologies at full capacity.

The determining factor of how much does AI cost and what is the machine learning engineer cost is the complexity of the project, thus, how much work should be done by the development team to create an AI/ML-based product. It is hard to estimate the amount of work and exact time frames compared to developing traditional software solutions. However, to calculate how much does machine learning cost approximately, here is a helpful roadmap to note:

  • Plan and analysis. At this phase, you need to clearly state what software you want to create, what problems it will solve, and what benefits it will offer to users. At this phase, you plan all the development stages and calculate the estimated cost for software creation and product launch.
  • Prototype development and evaluation. Typically, these costs come at about $25,000 of talking about complex projects. But it mainly depends on the complexity of the software you want to build so that this value may be less or more.
  • Minimum Viable Product (MVP). MVP is a real product that contains the necessary minimum of functions needed for the planned functionality. This MVP is exposed to a test group of users as a simplified version of the final product. This way, you can get feedback during the development stage and modify/improve/remove something to address the real needs of customers. MVP development cost starts from $25,000 to $100,000 and more.
  • Product launch. The cost of the last phase is usually calculated at the beginning. So, you get almost fixed costs for planning and launching the product, but be ready that the prototype and MVP development can result in bigger numbers.

According to TechReviewer, the minimum development cost of AI-based software built by one of the top 30 artificial intelligence companies varies from $5,000 to $100,000. The cost depends on the complexity of the project, industry, applicability, development time, and many other factors. Talking about how much does it cost to develop artificial intelligence software for each leading industry, we get the following results:

Computer software50,000+
Financial services30,000+
Information technology & services25,000+
Research & development10,000+
Web & mobile applications10,000+

As you can see, the cost of AI development depends on the industry a lot. While the mobile application development may cost much less than building a banking ML-based software, the amount of money needed for creating AI product is still affordable for progressive companies. However, ML and AI technologies are becoming more accessible to different businesses, so more ML/AI-based products are expected in all sectors in the future.

Machine Learning Developer Salary: Comparison of Cost to Hire Machine Learning Developers Worldwide

The current situation was well described by Quartz as a ‘space-race redux’, where countries go above and beyond just to get a breakthrough. We have examined Payscale and other resources for skills like ‘artificial intelligence’ and ‘machine learning’ in profiles:

 Software Engineer (AI)Software Engineer (ML)

* Ukrainian salaries are provided based on Mobilunity’s Recruitment Team research on the local job markets. All salaries are net and do not include the service fee (in case of hiring on a dedicated team model). The salaries are provided for comparison purposes and could be not entirely accurate. Contact us to know the exact cost of hiring a developer corresponding to the required parameters.

AI Engineer Salary

If we browse the same section for AI engineer salary, we will see that the United States dominates the market with the average annual AI developer salary of $87,241, when Germany offers  $77,890 as an average AI engineer salary. Canada provides a median AI development salary of $86,249, and UK offers AI software engineer salary of $73,698. AI developer salary is defined by both skills and experience a developer can offer. Such numbers make outsourcing seem like the best option, don’t they? Whereas in Ukraine such services can be found for as little as $30,000* of AI coder salary per annum. It means that compared to the US rates, a company can save more than $50K on AI engineer salary only.

hire machine learning programmer at mobilunity

Machine Learning Salary: How Much Does It Cost to Hire Machine Learning Developer?

While discussing machine learning developer salary, we can see that Germany and Canada tend to express more interest in developers with machine learning skills and experience. These charts offer a perspective on the average cost to hire Machine Learning developers, however senior machine learning engineer salary can reach up to $135,000 according to PayScale reports. For an overall comparison of machine learning developers by country, we will compare machine learning engineer salary with AI developer salary below:

al ml developers salaries comparison

The average machine learning engineer salary in the USA scores $144,132 per year which is 28% more compared to the reported average Machine Learning developer cost. Does it mean that one of the sources is wrong? Not at all! Typically, such margins are the results of emerging interest in the technology, when official statistics are slower than the changes on the market (applicable to both AI engineer salary and machine learning engineer salary).

As more and more businesses implement machine learning and artificial intelligence into their business practices, the need for professional, qualified developers continues. Ukraine remains a leader in the IT industry with certified IT professionals in different areas, like white label web developer, ML/ AL Engineer, and lower machine learning developer cost. Mobilunity, a leader in outstaffing and outsourcing professional IT services, is one of the most trusted providers of quality development services in Ukraine and a reasonable cost of machine learning programmer. With 15,000 IT professionals graduating annually in Ukraine, Mobilunity is a popular choice of employment for highly skilled and dedicated machine learning developers at a very fair cost of machine learning developer.

Take advantage of a fair cost of Machine Learning programmer at Mobilunity and find here your ideal ML coder!

All salaries and prices mentioned within the article are approximate NET numbers based on the research done by our in-house Recruitment Team. Please use these numbers as a guide for comparison purposes only and feel free to use the contact form to inquire on the specific cost of the talent according to your vacancy requirements and chosen model of engagement.

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