The fact that you are reading this page means that you likely want to hire a machine learning developer or an ML team. If this is true, you most likely want to use ML technology to address problems that will help your business get ahead, as so many other companies are doing.

You will need ML developers with strong programming abilities, relevant industry expertise, and a proactive can-do mentality. Finding and hiring ML developers can be a real challenge if you don’t know how.

The great news is that you’ve come to the correct location! In this post on how to find ML developers, I will share my deep insight into how to find the best ML developers.

I'll start by breaking down the costs associated with hiring ML engineers and the expenses tied to developing specific ML systems by complexity. After showcasing some ML project examples, I’ll share the top five companies to hire ML developers.

Within computer science, machine learning (ML) is one of the most exciting and rapidly developing fields. Researchers have been enthralled for decades with the question, "Can machines learn and improve from data?" We are finally arriving at the point where we are going to be able to learn just how much computers will be able to learn and improve.

Numerous corporate, governmental, and academic institutions have committed resources to the advancement of machine learning (ML) technologies over the years. With each passing year, more and more companies are jumping on board.  PwC estimates that by 2030, ML may boost the world economy by $15.7 trillion.

While this is seeing ML technology rapidly advancing, it does mean that machine learning developers are in very high demand. This is why it is so important to know how and where to hire machine learning engineers.

I will now show you how to do just that.

In this article:

• How Much Does It Cost to Hire a Machine Learning Engineer?
• How Much Does It Cost to Develop an ML Solution in 2026?
• What Kind of ML Developers Do You Need?
• 6 Steps to Hiring ML Developers

Latest Machine Learning Trends

Gartner anticipates a transformation in how we interact with ML and AI technologies. Gartner recently reported that "by 2028, one-third of interactions involving AI will utilize action models and autonomous systems to complete tasks."

These autonomous systems can perform tasks without constant human oversight, utilizing ML and AI technologies to make informed decisions that can give companies the edge on their competitors through a better understanding of their users.

Read more about "Building an AI Agent for Your Business: 7 Steps" recommended by the DevTeam.Space.

Factors that affect the cost of hiring a Machine Learning engineer:

When it comes to hiring ML developers, what should you expect to pay? Whether you're hiring full-time or part-time ML engineers, you'll find cost data available on a per-hour basis. These hourly rates can vary based on several factors:

• What is your hiring location? Costs tend to be higher in North America and Western Europe.
• Do you want to hire ML engineers from top-tier companies in the US? Expect to pay a premium.
• Will you need in-house ML engineers located in the US? This may come with higher hourly rates.
• Are you looking to hire remote ML developers from reputable US-based companies? You can anticipate fair value for your investment.
• Are you considering freelancers? While hourly rates may be lower, this option carries its own set of risks, which I'll elaborate on shortly.

How Much Does It Cost to Hire a Machine Learning Engineer?

If you’re hiring ML engineers in the US, it’s advisable to partner with a US-based company. This will ensure that you can enforce your legal agreements should there be any problems. An added bonus is cultural alignment, which will facilitate smoother collaboration with your ML team.

Machine Learning  developers for hire in the United States

An experienced or expert ML engineer will cost you $55-$300 per hour in the US. Your monthly costs will range from $10,400-$48,000 for a full-time ML developer.

Machine Learning developers for hire in Western Europe

In Western Europe, the hourly rate of an intermediate-to-expert ML engineer can range from $45-$200. Expect to pay $8,000-$32,000 per month if you hire such an experienced ML engineer full-time.

Eastern Europe

Hiring ML developers at the middle-to-advanced level can cost you from $35 to $150 per hour in Eastern Europe. You will need to pay $5,600-$24,000 for full-time ML specialists.

India

The hourly rate for intermediate-to-expert ML engineers in India is $10-$65. Prepare to pay $1,600-$10,400 per month if you hire such ML developers or ML consultants full-time.  

Cost of Machine Learning Engineers in the United States by State

When considering the cost of hiring a full-time ML developer in the United States, it’s important to note that these figures represent annual salaries and are generally lower than the rates for outsourcing developers on a temporary basis.

1. Hiring a Machine Learning engineer from California (San Diego, Los Angeles, San Francisco): $103,677 - $154,250 per year

2. Hiring a Machine Learning engineer from Colorado (Denver, Colorado Springs, Greenwood Village): $111,000 - $124,000 per year

3. Illinois (Chicago, Buffalo Grove, Deerfield): $106,228 - $123,440 per year

4. Maryland (Fort Washington, Baltimore, Annapolis): $90,001 - $115,000 per year

5. Machine Learning developers from Massachusetts (Boston, Worcester, Springfield): $112,500 - $113,875 per year

6. Machine learning developers from New York (Albany, New York, Brooklyn): $112,500 - $166,400 per year

7. Hiring a Machine learning professional in Texas (Austin, Houston, Dallas): $93,917 - $121,811 per year.

How Much Does It Cost to Develop a Machine Learning Solution?

The costs associated with developing ML solutions largely depend on the complexity of the project. For instance, creating a simple ML chatbot will generally be less expensive than developing a medium-complexity ML application, such as a virtual assistant, or a complex ML project, such as a driving assist system. Ultimately, the expenses for complex ML projects can escalate significantly.

In a 2024 post, Elon Musk stated that “Tesla's cumulative investment in its self-driving program would be well over $10 billion this year”. This is how much some companies are spending on ML and AI-based systems.

Simple ML Projects
Consider a scenario where you're developing an ML system using off-the-shelf tools. You would design and deploy models utilizing standard machine learning algorithms backed by organized, high-quality, and labeled datasets. This would be categorized as a simple ML development project. Such a project might range from $5,000 to $50,000 and would likely require between 1 to 3 ML developers. This project would likely take 1 to 8 weeks to complete.

Medium-Complexity ML Software Development Projects
Imagine your ML project involves multiple capabilities, such as supervised learning, natural language processing, and computer vision. If the project requires developing medium-complexity ML models and relies solely on unlabelled data for training, it would fall into the medium-complexity category. Expect costs of somewhere between $50,000 and $250,000 and a timeframe of 1.5 to 6 months. This kind of project typically requires between 2 and 6 machine learning engineers.

Complex ML Software Development Projects
Complex ML projects will involve sophisticated models and advanced techniques like deep learning. They could require extensive data preparation, including unlabelled datasets such as visual recognition data from cameras that make up a driver-assist system. These projects often need specialized expertise and can cost significantly more than medium-complexity ones, requiring a team of seasoned ML engineers over an extended timeframe.

Defining Your ML Application Features

Your app must stand out to succeed in the competitive ML application market. If you created an ML application that mirrored all competitors' features, what incentive would users have to choose yours?

Developing a comprehensive feature list is essential for visualizing your ML app and its goals and, subsequently, allowing you to gauge your budgeting and hiring needs. Document all desired features and goals to create a clear product outline, which will guide you in hiring the right ML development team.

What Kind of Machine Learning Engineers Do You Need(Python vs R)

If you’re updating an existing ML application that was built in R or need deep integration with R-based libraries, hiring an R developer is necessary. However, many newer developers specialize in Python, which is the preferred language for contemporary ML development due to its extensive libraries and community support.

Python vs. R engineers: Pros and Cons

The primary difference is that Python allows developers to write streamlined code, eliminating the need for verbose syntax such as braces and semicolons. This means that a Python developer can focus on creating clean and maintainable code. This simplicity is especially beneficial for junior developers, as it reduces the chances of making errors that can lead to bugs.

Note: I strongly advise against using junior ML developers in your project. Experience is key when it comes to ML development; don’t risk hiring junior ML developers.

Python also includes several innovative features, such as dynamic typing and powerful libraries, which contribute to cleaner code with fewer issues. In contrast, languages like R can complicate memory management, making it a challenging task for developers. Python addresses this with its garbage collection system, which automatically manages memory by deallocating objects that are no longer in use.

Another crucial point to consider is support. While R is still supported by the community, the level of investment has decreased significantly in recent years. This is primarily due to the growing popularity of Python, leading to fewer libraries being developed for R. Over time, this waning interest may result in reduced support for R by its maintainers.

It is for this reason that I strongly recommend using Python.

Backend Service Providers
Selecting a reliable backend service provider is essential for hosting your ML application, along with choosing the best backend developers who possess all the expertise to write the necessary code and set up your system.

Naturally, you'll want fast performance and reliability from your ML system and supporting web and mobile apps. Fortunately, many service providers meet these needs, though some stand out more than others. Here are my top picks for backend service providers.

Google Cloud Platform (GCP)
Google Cloud Platform offers an extensive range of products and services, including powerful tools like Google Compute Engine and TensorFlow for machine learning projects. Its robust AI and ML capabilities make GCP an excellent choice for those developing machine learning solutions.

Amazon Web Services (AWS)
AWS continues to be the world's leading backend service provider. Thanks to the foresight of its founder, Amazon was an early player in cloud infrastructure, allowing it to dominate the market. AWS provides a vast array of powerful tools, including AWS Lambda, which facilitates scalable machine-learning applications.

IBM Cloud
IBM Cloud is a strong backend provider, particularly for enterprises. It offers a variety of tools, including Watson for AI and machine learning, as well as Red Hat OpenShift for hybrid cloud deployments. These tools allow developers to create scalable ML applications that can be deployed in diverse environments.

Microsoft Azure
Microsoft Azure is widely used by Fortune 500 companies due to its comprehensive suite of tools and technologies. Azure provides IaaS, PaaS, SaaS, and serverless options tailored for both ML and application development, making it a versatile choice for backend developers and ML developers.

Additional Machine Learning Developer Skills

• Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Knowledge of IDEs like Jupyter Notebook, PyCharm, or Visual Studio Code.
• Familiarity with app development frameworks and libraries, such as Flask, FastAPI, and Django.
• Understanding of CI/CD pipelines and tools like Jenkins, Azure DevOps, or GitLab.
• Knowledge of data visualization tools and libraries, such as Matplotlib or Seaborn.
• Familiarity with relevant project management methodologies like Agile and Scrum.
• Ability to quickly adapt to your project management tools.
• Strong communication skills.
• Excellent problem-solving capabilities and critical thinking skills.

Hiring Machine Learning Engineers: 6 Steps

Here are the 6 essential steps to hire a machine learning engineer:

Step 1: Define Project Requirements

Your machine learning project requirements encompass the specific functionalities, features, and goals that your project must meet for success. These requirements are crucial and should be established before entering the design and development phases.

While your project’s requirements may evolve during development, especially after launch, as you gather user feedback, the overall goals and core functionalities are likely to remain constant.

Take the time to mentally walk through the process of using your application, focusing on your specific objectives. If you are building a chatbot, then what level of engagement are you hoping for? Will your users expect short and concise answers, or will they want long format and detailed answers, etc.?

Create a list of both functional and non-functional requirements, also considering aspects like scalability, performance, and security.

Once your project requirements are clear, you’ll have a better understanding of the type of ML engineers and backend developers you will need.

What Type of Developers Will I Need for a Machine Learning Project?
The intricacy of your project and the tech stack you choose will determine how many and what kind of developers and data engineers you will need.

For a typical machine learning project, you would most likely need one or more mid-level or senior Python engineers with knowledge of machine learning frameworks such as TensorFlow or PyTorch. Multiple developers, a data scientist, and possibly a UI/UX designer may be required for mid- or enterprise-level projects.

You will also need experts in those fields if your project calls for specialist technologies like data engineering, natural language processing, or deep learning.

If you're unsure of your needs, consider speaking with one or more machine learning development specialists. Making contact with ML-focused software development firms will likely yield very insightful information.

Step 2: Software Development Company or Freelancer Platform?

Create a job description template when you've decided on the specifics of your project and post it to freelancing websites or job boards. This template will also make contacting software development companies easier. Let’s now look at how these platforms differ.

If you do not want to hire a machine learning engineer full-time, you can hire from a software development business or a freelancer platform.

Hire Machine Learning Developers from a Software Development Company
The safest option for hiring developers is to work with a dedicated software development company that provides full-time staff on an outsourced (temporary) basis to client companies.

Advantages of hiring machine learning developers from a software development company include:

• Access to a wide range of expert developers.
• Developers who are fully vetted and work for the company full-time.
• Experience with similar projects enhances the likelihood of success.
• Assignment of an account manager to facilitate communication and project management.
• Inclusion of a quality assurance (QA) professional to oversee the project before launch.
• Access to project management tools and processes.
• Contracts that safeguard your intellectual property (IP) rights.
• Risk-free trials allow you to evaluate a developer’s fit without a long-term commitment.

Hire Machine Learning Engineers from a Freelancer Platform
Freelancer platforms may be a viable option for small-scale projects on a tight budget.

However, the downsides include:

• Freelancers often juggle multiple projects, leading to part-time commitments.
• A time-consuming vetting process to check past work.
• The risk of freelancers abandoning your project for more lucrative opportunities.
• Potential issues with intellectual property security.
• Lack of project management tools or oversight.

I recommend being cautious with machine learning freelancers, as many companies encounter significant challenges when their hired freelancers lack the necessary skills or leave unexpectedly. Opting for a professional software development company can save you from these headaches.

Step 3: Interview Your Machine Learning Candidates

You should conduct three interviews: a primary interview, a skills interview, and a final interview.

Primary Interview:
Use this initial interview to narrow down your candidate list. Discuss foundational knowledge, communication skills, problem-solving approaches, and motivation for your project.

Important questions to ask include:

• Are they familiar with Agile or other development methodologies?
• Can they work with your specific tools or task trackers?
• What communication platforms do they prefer (e.g., Slack)?
• Do they have a GitHub repository, or can they access yours?

Ensure remote developer candidates can overlap with your working hours by at least four hours for effective communication during planning and problem-solving.

Skills Interview:
This interview assesses the technical skills of your machine learning candidates. If you're not experienced in ML development, consider involving a professional who can help evaluate candidates' technical answers.

You might also consider assigning a coding problem that is relevant to your project. This will allow you to gauge their coding style and familiarity with necessary frameworks.

Final Interview:
By this stage, you should have narrowed it down to three or four candidates. This will be the most in-depth interview, focusing on their ability to meet your project’s specific needs.

For example, ask how they would implement a machine-learning model for a particular feature. Gauge their understanding of the tools and technologies they would employ and discuss potential challenges and solutions.

Step 4: Carefully Review Their Past Projects

It’s critical to thoroughly check references and past projects. Encourage candidates to demonstrate their previous work during the final interview to validate their skills. Look for relevant projects that showcase their expertise in machine learning and their problem-solving abilities.

Step 5: Onboard Your Machine Learning Engineer

After you've decided on a candidate, give it some thought to be sure you made the best decision.

Before providing project details, have them sign a non-disclosure agreement (NDA) to start the onboarding process. Give them the equipment they need and give them a tour of your development team. Clearly define your expectations and development approach, as well as communication channels.

Give them all the tools they require, such as project schedules, paperwork, and any pertinent training materials, so they can begin contributing successfully right away.

Step 6: Set Expectations and Goals

Setting clear objectives and goals for the machine learning engineer is crucial after onboarding. Specify their roles within the project and include deadlines and deliverables. Plan frequent check-ins to keep an eye on things, handle obstacles, and make sure the project's goals are being met.

By following these procedures, you should be able to effectively incorporate your new machine learning developer into your project, maximizing their contributions and guaranteeing the accomplishment of your machine learning project.

That is all. Your ML developer can immediately begin working on your project.

5 Best Companies to Hire a Machine Learning Engineer

Finding the right machine learning developer for your project can be challenging, but knowing where to look can make the process smoother. Here are the top five companies to consider when hiring machine learning engineers in 2026.

1. DevTeam.Space 

DevTeam.Space is a community of 1,200 developers and 62 development teams based in the United States and Europe. All its full-time developers are thoroughly vetted by the company and work under NDAs when requested.

The company boasts a remarkable 99% success rate after completing hundreds of projects. Notable clients include Disney, Airbus, and NEC, which have partnered with DevTeam.Space to create next-generation applications using machine learning and artificial intelligence technologies.

DevTeam.Space’s success can be attributed to its unique AI-powered Agile process and the assignment of dedicated account managers to every developer, designer, and development team they outsource. The company also provides a free quality assurance engineer to check the finished product before release.

2. Arc

Arc is a remote freelance platform that connects freelance software engineers, including machine learning specialists, with companies looking to hire them. The platform claims to have 350,000 developers worldwide. However, access to vetted developers requires a subscription.

Current and past employee reviews on Trustpilot are predominantly positive. Arc promises to connect client companies with suitable machine learning developers within 72 hours, making it a fast option for hiring.

3. Fiverr

Fiverr is an Israeli-based freelancer platform that serves a wide range of freelance skills, including machine learning. Within its structure, there is a dedicated section for machine learning developers, meaning once you post your job description, you're likely to receive multiple applications.

While it provides a broad pool of candidates, Fiverr’s business model means you’ll need to conduct thorough evaluations of the applicants to ensure they meet your project requirements.

4. Upwork

Upwork is the world's largest freelancer platform, featuring millions of freelancers across various domains, including machine learning and data science.

Setting up an account is straightforward—just post your job description and begin interviewing candidates. Keep in mind that Upwork takes a 10-20% fee from all freelancers, so factor this into your pay rate, as lower pay rates demoralize freelancers.

5. Toptal

Toptal claims to allow only the top 3% of freelancers onto its platform, providing an extra layer of assurance that you'll be receiving high-quality talent for your machine-learning project.

This software development-specific platform enables you to hire machine learning engineers, data scientists, and AI specialists, ensuring you have access to a curated talent pool that can meet your technical needs.

Conclusion

Finding the ideal machine learning engineer for your project is not that hard when you know where to look. At DevTeam.Space, there is a vibrant community comprising 62 expert development teams and over 1,200 skilled developers.

The platform has partnered with some of the biggest companies in the world and numerous innovative startups. By submitting your project request, DevTeam.Space will match you with a suitable machine learning engineer within just 72 hours, allowing you to move forward with your project development.

DevTeam.Space offers transparent pricing details and a risk-free trial to all clients. In the rare instance that you are not 100% satisfied with your developer, they provide a replacement at no extra cost. However, with over 350 projects completed and a success rate of 99%, such instances are highly unlikely.

When you engage machine learning engineers from DevTeam.Space, you're assigned a dedicated account manager with extensive experience in managing projects similar to yours. Additionally, a complimentary quality assurance engineer is included to test all new features and pre-release applications, ensuring optimal performance and security of your finished product.

For those in need of expert machine learning development services, simply fill out their user-friendly hiring form. Their dedicated tech account managers will promptly reach out to schedule a complimentary discovery call to answer any questions you may have.

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