Toptal is a marketplace for top artificial intelligence engineers, programmers and experts. Top companies and startups choose Toptal’s AI engineers for their mission critical software projects.
Robby is a machine learning expert with 10+ years of experience in research and back-end software development for machine learning solutions. With master's degrees in computer science and artificial intelligence in addition to his Ph.D. in computer science, Robby is well equipped to provide solutions to a variety of issues in companies of all sizes.
Jared has 10+ years of experience in data science, analytics, and machine learning, serving a wide range of clients, from startups to Fortune 500 companies. He's a former founder and holds a M.Sc. in applied physics. His projects include uplift marketing models driving 9%+ lift for 100+ million customers, big data analytics reducing $10+ million in annual OpEx, automated collectible appraisal using computer vision, autonomous drones, and $1+ billion climate resiliency planning.
Darron has 15 years of experience in R&D and client-facing roles as lead engineer of mission-critical artificial intelligence and machine learning and advanced analytics software for notable clients in global media, renewable energy industries, and the U.S. Department of Defense. Darron holds a BSc in computer science and an MSc in data analytics engineering with disciplines in AI and machine learning, decision optimization/mathematical modeling, and computational statistics.
François is a seasoned leader with experience building data platforms and machine learning solutions at major technology companies and startups in B2C and B2B settings. François has spent seven years at Spotify building data infrastructure teams as a manager and leveraging machine learning techniques to improve the music catalog as a staff engineer.
Martin is an experienced data scientist with vast knowledge of machine learning and artificial intelligence. After completing his Ph.D. in physics, he has worked on a wide range of projects from both the private and public sectors. He has extracted meaningful insights throughout the years and created products leveraging data sources in the most varied formats, including images, geospatial data, structured databases, time series, and text.
Jorge is a deep learning researcher and engineer with a PhD in computer science and artificial intelligence and an MBA. He's successfully worked with startups based in San Francisco, London, and Madrid. Along with an expertise in AI, Jorge has several years of experience in the industry, including founding and running his own tech company. Since 2015, he’s been focusing primarily on remote roles.
Karla is a bilingual professional with a PhD in Artificial Intelligence and 10+ years of experience in developing outstanding computer vision, machine learning, and AI technologies as a dedicated research scientist. As a motivational leader, she thrives in building research agendas and managing complex projects to provide world-class products, systems, and platforms. She also forges lasting relationships and uses out-of-the-box thinking to drive cutting-edge research efforts.
Kartik is a senior full-stack AI engineer. His work seamlessly fuses software engineering and artificial intelligence, enabling him to develop smart, scalable, and impactful solutions. Kartik takes a keen interest in emerging technologies, integrating them into production-ready systems to give his clients a competitive edge. Staying ahead of the curve in the ever-evolving tech landscape is crucial for him.
Sheng Han is a senior machine learning (ML) engineer with 10 years of experience in the research, development, and application of various ML, AI, and generative AI solutions. He has turned big data into valuable actions and has a demonstrated history of driving business efficiencies and cost reductions. Sheng is proficient in generative AI, prompt engineering, computer vision, natural language processing (NLP), anomaly detection, and prediction tasks.
Naoki is a senior machine learning engineer with experience in PyTorch. He is passionate about deep learning training, and he worked on model quantization and neural architecture search for vision models. Naoki is also an experienced C++ programmer who has worked on real-time algorithmic trading systems.
Pawel is an experienced data-scientists and machine learning professional. He has worked for Fortune 100 companies, and he has an academic background in the field. Before moving to data science, he was a former lead architect in Samsung R&D Center. Pawel holds a Ph.D. in knowledge representation and reasoning as well as a master's degree and a bachelor of science degree in computer science.
AI developers are versatile experts who improve business systems and processes, automate tasks, build AI models, and perform statistical analyses. Pinpoint the top candidates for your business with this guide to hiring AI developers, including skill requirements, tips on writing the job description, and sample interview questions.
... allows corporations to quickly assemble teams that have the right skills for specific projects.
Despite accelerating demand for coders, Toptal prides itself on almost Ivy League-level vetting.
Our clients
Creating an app for the game
Leading a digital transformation
Building a cross-platform app to be used worldwide
Drilling into real-time data creates an industry game changer
Testimonials
Tripcents wouldn't exist without Toptal. Toptal Projects enabled us to rapidly develop our foundation with a product manager, lead developer, and senior designer. In just over 60 days we went from concept to Alpha. The speed, knowledge, expertise, and flexibility is second to none. The Toptal team were as part of Tripcents as any in-house team member of Tripcents. They contributed and took ownership of the development just like everyone else. We will continue to use Toptal. As a startup, they are our secret weapon.
Brantley Pace
CEO & Co-Founder
I am more than pleased with our experience with Toptal. The professional I got to work with was on the phone with me within a couple of hours. I knew after discussing my project with him that he was the candidate I wanted. I hired him immediately and he wasted no time in getting to my project, even going the extra mile by adding some great design elements that enhanced our overall look.
Paul Fenley
Director
The developers I was paired with were incredible -- smart, driven, and responsive. It used to be hard to find quality engineers and consultants. Now it isn't.
Ryan Rockefeller
CEO
Toptal understood our project needs immediately. We were matched with an exceptional freelancer from Argentina who, from Day 1, immersed himself in our industry, blended seamlessly with our team, understood our vision, and produced top-notch results. Toptal makes connecting with superior developers and programmers very easy.
Jason Kulik
Co-founder
As a small company with limited resources we can't afford to make expensive mistakes. Toptal provided us with an experienced programmer who was able to hit the ground running and begin contributing immediately. It has been a great experience and one we'd repeat again in a heartbeat.
Stuart Pocknee
Principal
How to Hire AI Engineers through Toptal
1
Talk to One of Our Industry Experts
A Toptal director of engineering will work with you to understand your goals, technical needs, and team dynamics.
2
Work With Hand-Selected Talent
Within days, we'll introduce you to the right AI scientist for your project. Average time to match is under 24 hours.
3
The Right Fit, Guaranteed
Work with your new AI engineer for a trial period (pay only if satisfied), ensuring they're the right fit before starting the engagement.
Find Experts With Related Skills
Access a vast pool of skilled developers in our talent network and hire the top 3% within just 48 hours.
AI developers are pivotal in crafting intelligent systems and data-driven solutions. Toptal’s AI experts design machine learning models, natural language processing (NLP) systems, and neural networks to create predictive analytics, automate processes, and deliver personalized experiences tailored to real-world applications.
Machine Learning
Machine learning models can automate complex tasks, recognize patterns, and make predictions from data. Toptal’s AI developers use frameworks like TensorFlow, PyTorch, and scikit-learn to create and train models that drive automation, analyze large datasets, and produce accurate insights tailored to your business needs.
Natural Language Processing
NLP enables applications to understand and interact with human language. In order to enhance customer interactions with AI-driven communication, Toptal developers build NLP systems for chatbots, sentiment analysis, and language translation, helping businesses interpret user input and respond effectively.
Neural Networks
Neural networks enable advanced AI applications by mimicking the structure of the human brain. Toptal developers design and train deep learning models, such as convolutional and recurrent neural networks, for complex tasks like speech recognition, image analysis, and autonomous systems.
Computer Vision
With computer vision, applications can interpret, analyze, and make decisions based on visual data, such as images, videos, and live camera feeds. Our developers use AI techniques to create object detection, facial recognition, and image classification applications, enabling automated analysis and advanced visual recognition capabilities.
Automating Business Processes
Many industries rely on repetitive, time-consuming tasks that can slow down productivity and strain resources, from data entry and customer support to inventory management and quality control. Our developers design automation solutions that improve operational efficiency, reduce costs, and free up human resources for higher-value work, helping businesses run more effectively.
Predictive Analytics and Forecasting
In predictive analytics, AI is used to anticipate future trends based on historical data. Toptal’s AI developers build models that analyze past data and generate accurate forecasts, empowering businesses to make data-driven decisions, plan proactively, and stay ahead of market trends.
Developing Recommendation Systems
Personalized recommendation engines enhance user experience and boost engagement. Leveraging the power of large language models (LLMs), our developers create AI-driven recommendation systems tailored to e-commerce, media, and content platforms, delivering recommendations that increase user satisfaction and conversion rates.
Optimization and Deployment
Because of their complexity, AI models must be optimized to ensure the highest performance, precision, and computational efficiency. Our developers fine-tune models for the highest accuracy, deploying them to real-world environments in a manner that is reliable, scalable, and easy to maintain.
Integrating AI into Existing Systems
Equipping current software with AI capabilities transforms its ability to process data, make informed predictions, and automate complex tasks. Toptal developers integrate AI into existing platforms, enhancing your systems with data-driven insights, automated workflows, and intelligent decision-making features, resulting in smarter and more efficient applications.
Ethical AI and Bias Mitigation
To build trust, AI should be fair and transparent, particularly in sensitive applications like healthcare, hiring, finance, and law, where biased outcomes can impact people’s lives. Our developers address ethical concerns by identifying and mitigating bias, ensuring AI models operate responsibly, promote fairness, and align with ethical standards.
FAQs
How much does it cost to hire an AI engineer?
The cost associated with hiring an AI engineer depends on various factors, including preferred talent location, complexity and size of the project you’re hiring for, seniority, engagement commitment (hourly, part-time, or full-time), and more. In the US, for example, Glassdoor’s reported average total annual pay for AI engineers is $154,000 as of November 2024. With Toptal, you can speak with an expert talent matcher who will help you understand the cost of talent with the right skills and seniority level for your needs. To get started, schedule a call with us — it’s free, and there’s no obligation to hire with Toptal.
How do I hire an AI engineer?
To hire the right AI engineer, it’s important to evaluate a candidate’s experience, technical skills, and communication skills. You’ll also want to consider the fit with your particular industry, company, and project. Toptal’s rigorous screening process ensures that every member of our network has excellent experience and skills, and our team will match you with the perfect AI engineers for your project.
How high is the demand for AI engineers?
The demand for talent skilled in AI is surging, as 77% of consumer services or devices are powered by AI, according to Pega AI’s global consumer survey. With the increasing scope and accuracy of models, the proliferation of generative AI, and advances in natural language processing (NLP) and computer vision (CV), it is pertinent, if not urgent, to have an AI engineer on your team. The field of AI only continues to grow—with global AI spending expected to surpass $631 billion by 2028, according to IDC—and AI developers will help businesses remain competitive.
How to choose the best AI engineers for your project?
To choose the best AI expert for your project, you should assess a candidate’s proficiency in programming, data science, mathematics, deep learning frameworks, and cloud computing. They should possess a mastery of one or more programming languages (e.g., Python, R, Java, or C++) and a solid understanding of computer science fundamentals. The required data science skills include knowledge of data structures, algorithms, and ML models, and the necessary mathematical background includes knowledge of linear algebra, calculus, and statistics. Finally, AI experts should have experience with the top deep learning frameworks (e.g., TensorFlow, PyTorch, Keras) and cloud computing platforms (e.g., Amazon Web Services, Microsoft Azure, Google Cloud Platform) to deploy AI models at scale.
How quickly can you hire with Toptal?
Typically, you can hire an AI engineer with Toptal in about 48 hours. For larger teams of talent or Managed Delivery, timelines may vary. Our talent matchers are highly skilled in the same fields they’re matching in—they’re not recruiters or HR reps. They’ll work with you to understand your goals, technical needs, and team dynamics, and match you with ideal candidates from our vetted global talent network.
Once you select your AI engineer, you’ll have a no-risk trial period to ensure they’re the perfect fit. Our matching process has a 98% trial-to-hire rate, so you can rest assured that you’re getting the best fit every time.
Why do we need artificial intelligence?
AI has become increasingly important in recent years and has various real-world applications. It can be used for chatbots, fraud detection, risk assessments, medical diagnoses, personalized marketing and customer services, image recognition, autonomous vehicles, customized learning experiences, and video games. Across most industries, AI can accelerate and optimize business processes and automate tasks with the help of an experienced AI consultant.
How are Toptal AI engineers different?
At Toptal, we thoroughly screen our AI engineers to ensure we only match you with the highest caliber of talent. Of the more than 200,000 people who apply to join the Toptal network each year, fewer than 3% make the cut.
In addition to screening for industry-leading expertise, we also assess candidates’ language and interpersonal skills to ensure that you have a smooth working relationship.
When you hire with Toptal, you’ll always work with world-class, custom-matched AI engineers ready to help you achieve your goals.
Can you hire AI engineers on an hourly basis or for project-based tasks?
You can hire AI engineers on an hourly, part-time, or full-time basis. Toptal can also manage the entire project from end-to-end with our Managed Delivery offering. Whether you hire an expert for a full- or part-time position, you’ll have the control and flexibility to scale your team up or down as your needs evolve. Our AI engineers can fully integrate into your existing team for a seamless working experience.
What is the no-risk trial period for Toptal AI engineers?
We make sure that each engagement between you and your AI engineer begins with a trial period of up to two weeks. This means that you have time to confirm the engagement will be successful. If you’re completely satisfied with the results, we’ll bill you for the time and continue the engagement for as long as you’d like. If you’re not completely satisfied, you won’t be billed. From there, we can either part ways, or we can provide you with another expert who may be a better fit and with whom we will begin a second, no-risk trial.
Tetyana is an AI expert who has served as a founder, chief data scientist, and consultant for clients in several countries. She has worked on projects for large companies like MultiChoice Group and Control Risks in industries including energy, government, education, and biotechnology. Tetyana has built systems for finance and accounting purposes, ML-powered NLP, forecasting, and anomaly detection.
Companies Clamor to Hire AI Experts As Demand Surges
Today, 77% of users interact with a service or device powered by artificial intelligence. AI applications include chatbots, video surveillance tools, object detection applications, and autonomous systems (e.g., humanoid robots and self-driving cars). AI can help us make more sensible financial decisions and improve our health and wellness. This is just the start: As AI use cases expand and automate repetitive tasks, global AI spending is projected to surpass $500 billion by 2027. In this surge, practically all businesses can benefit from hiring the right AI engineers.
With AI courses and terminology becoming more commonplace among professionals, you may assume that there exists a large talent pool, and hiring an AI engineer is easy. It is not. There’s a big difference between the many software engineers who list AI as a skill on their résumés and capable AI engineers who can add value to a company by building cutting-edge AI systems, integrating them into existing company infrastructure, and guaranteeing they work efficiently.
So, how can you hire artificial intelligence developers who will give your enterprise an AI advantage? Read on to discover the critical skill requirements, tips on crafting an effective job description, and interview questions that will help you identify exceptional candidates.
What Attributes Distinguish Quality AI Developers From Others?
Strong candidates are well versed in the required core technical skills for AI development:
They have experience developing machine learning (ML) or AI models, and generative AI solutions, from scratch.
They provide scalable solutions with API development (i.e., turning models into APIs).
They understand ML algorithms.
They incorporate DevOps processes into the ML and AI development workflow, and deploy models in a continuous learning pipeline.
They maintain ML and AI systems infrastructure.
They can work with big data and perform statistical analysis of data.
Proficiency in at least one programming language (e.g., Python, R, Java, MATLAB) is a must when working with AI. Python is particularly popular for AI development due to its many libraries and ease of use. Strong software development skills will allow engineers to collaborate effectively with teams and implement solutions with the reliability and accuracy required for a production environment.
Additionally, artificial intelligence experts should be familiar with modern large language models (LLMs) and deep learning frameworks (TensorFlow, PyTorch, and Keras), as well as cloud computing platforms (AWS, Microsoft Azure, and Google Cloud Platform). This will ensure that they can deploy AI models at scale and serve enterprise-level projects.
The best AI engineers combine their technical abilities with soft skills—specifically, the ability to evaluate and solve the complex problems that businesses face. Experts who can speak to experience in this area (e.g., designing or contributing to an AI strategy) are top candidates. These engineers recognize the potential of AI technologies to bring unprecedented progress and massive cost savings to organizations.
Look for seasoned AI professionals with several years of experience in your industry or across multiple sectors. Review the projects they have worked on and completed to confirm their fit. Many candidates list their portfolio projects on their résumés, though you may also request that they provide GitHub links with their application or in a cover letter. Publicly available portfolio projects highlight the experience and accomplishments of top AI engineers.
How Can You Identify the Ideal AI Developer for You?
AI engineers often focus on different specialization areas, and you should choose a developer whose skill set aligns with your project goals.
Individual candidates may have varying levels of expertise across each specialization area. Still, overall, an AI engineer should possess most of these skills and be an expert in at least one area:
Specialization
Required Skills
Applications
Mathematics and statistics
Comprehension of core mathematical concepts such as linear algebra, calculus, probability, and statistics
Designing an AI system
Correcting errors
Improving an out-of-the-box model’s performance for your particular problem
Machine learning
Knowledge of ML algorithms (e.g., decision trees, random forests, neural networks, and deep learning)
Experience with ML libraries (e.g., scikit-learn, TensorFlow, and PyTorch)
Building efficient models
Experience with fine-tuning models for specific domains
Making appropriate decisions when choosing the tools and algorithms to solve the problem at hand
Data structures and algorithms
Solid understanding of fundamental data structures (e.g., arrays, linked lists, and trees)
Experience with standard algorithms (e.g., sorting, searching, and optimization algorithms)
Preparing data
Extracting relevant features from data
Feeding the most important features in the model to ensure the model’s performance
Data analysis
Experience with data science and data analysis techniques (e.g., data cleaning, normalization, and feature extraction) and tools (e.g., pandas and NumPy)
Extracting insights from data
Presenting problems and solutions to stakeholders
Translating real-world problems into mathematical representations
Databases
Familiarity with SQL databases and NoSQL databases (e.g., MongoDB and Cassandra)
Experience managing and storing large datasets
Handling data efficiently
Ensuring the transparency of AI algorithms
Presenting project results and intermediate stages effectively
Integrating with upstream and downstream systems
What is the difference between AI and ML developers?
One common challenge for hiring managers is understanding the difference between AI and ML engineers, especially since AI engineers need various ML skills and may specialize in this area. How do artificial intelligence versus machine learning developers compare? The two professions require similar training, aptitudes, and academic backgrounds (typically in statistics, mathematics, computer science, or engineering). Both of these types of experts may build ML models, analyze data, and build ML pipelines.
However, the role of an AI engineer is broader than that of an ML engineer. It can require the ability to build automated systems based on ML models, and AI engineers may be more involved in computer or data engineering tasks. AI engineers may also be responsible for setting the direction of a corporation’s AI strategy and managing the AI infrastructure. Unless you are looking to hire machine learning engineers, target AI developers for hire who possess the specialization that matches your requirements.
How to Write an AI Developer Job Description for Your Project?
You’ll need to be clear on your organization’s needs to attract skilled developers with your job description. Some companies want engineers who can optimize their organization’s use of artificial intelligence and machine learning, while others look for experts to design and implement AI solutions from scratch. Consider whether you need an AI consultant to define a new strategy or a full-time staff member who will be deeply involved in all aspects of your corporate operations.
Next, write your job description with critical project details in mind. Describe the project’s budget, timeline, relevant business context, and existing software technologies. Identify the desired outcomes you want from the AI engineer’s work, and provide an overview of the development team the new employee will join. It is also helpful to specify whether you’re looking for on-site, hybrid, or remote AI developers.
With a comprehensive job description complete, the next part of the hiring process is selecting high-quality candidates and interviewing them to assess their fit with your other team members.
What Are the Most Important AI Developer Interview Questions?
To conduct an effective interview, focus on the questions relevant to your business industry and those testing the applicant’s knowledge of the AI ecosystem and processes your AI project will use.
Regardless of project or industry needs, AI experts should be able to enunciate the core value provided by AI and how it works, as these skills are crucial to convincing stakeholders of the importance of AI solutions. The following examples provide a model for these types of questions and how you might expect experienced developers to approach interview answers:
How does AI work?
In general, AI can be defined as software that mimics human thinking and decision-making. It works by using actual or artificially created data to match questions to potential answers based on ML and statistical algorithms. These questions and answers include both those posed in natural language and a wider set of questions like determining the next location of a vehicle or recognizing objects in a video clip. In the past, AI was used to create expert systems by assembling all possible answers to all possible questions, a method with storage and time constraints. But new ML advances have allowed for a strategy that provides questions and answers that are not an exact match but a probabilistic pair, allowing AI to answer a broader range of questions—albeit with varying accuracy.
What are the main applications of AI?
While the range of AI capabilities is limited in specific ways compared to human intelligence, AI solutions are highly efficient in natural language processing (NLP), computer vision (CV), and classical simulation and optimization problems. AI can be used in a wide variety of applications, including sentiment analysis, predictive analytics, automatic speech recognition, and more. It can even help programmers write code. Experienced developers should understand the best ways to help your organization optimize processes and build AI solutions based on your industry and products.
Why is AI important?
AI can potentially add up to a 14% GDP boost globally by 2030. It is already widely used by many businesses in healthcare, crime systems, knowledge synthesis, transportation, security, and finance. Artificial intelligence experts who will spearhead new AI initiatives should be able to speak to existing AI uses and benefits to convince stakeholders of its importance.
Since AI solutions vary across industries and project needs, it is vital to ask a developer how they plan to address your specific problems.
What type of model would you recommend for our business needs?
An engineer should choose an algorithm and appropriate tools based on a company’s specific problem and data. For instance, certain models (e.g., decision trees) are suitable for handling tabular data resembling spreadsheets, while others (e.g., deep neural networks) excel at processing unstructured data like images or audio.
How would you measure the performance of your AI solution?
Candidates must always keep in mind the objective of delivering tangible business value and establish a means of quantifying that value. Look for developers who provide a specific plan for measuring the performance of machine learning models with business-relevant metrics. For instance, when predicting customers at risk of churn, it is essential to involve subject matter experts from the business domain. Their insights can help identify client segments that generate higher revenue, ensuring that the model excels at detecting such valuable customers. Merely detecting at-risk clients with low value would not prove useful in this scenario.
How will you ensure your model’s quality over time?
The performance of artificial intelligence and machine learning models can vary over time as the underlying context and circumstances for which the models were trained evolve. For example, a revenue prediction model may have experienced a significant increase in errors during the onset of the COVID-19 pandemic. Candidates should outline a clear monitoring plan to track the performance of ML models over time and promptly identify any degradation in their performance.
After assessing a developer’s understanding of broader and project-specific AI topics, you should supplement these questions with additional ones that are specific to your desired AI specialization:
There is no one-size-fits-all answer for how to prepare your interview questions, especially when hiring AI experts. Tailoring your topics to your specific project requirements will go a long way in ensuring you hire a quality engineer.
Why Do Companies Hire AI Engineers?
AI will undoubtedly change the future. It has the potential to automate routine manual tasks and help with strategic decision-making, saving companies considerable money. AI may disrupt key industries like consumer marketing, finance, and healthcare: It can personalize the user experience for consumer products, power financial algorithms impacting markets and risk management, and improve healthcare diagnoses (e.g., AI-powered personal wellness assistants that monitor our health or computer vision systems that assist with surgery and disease diagnosis).
The increasing use of AI—and the resulting demand for talent—is clear, but what does an artificial intelligence engineer do, exactly? AI developers are the force behind a growing variety of real-world AI applications: task automation, chatbots, fraud detection, risk assessments, medical diagnoses, personalized marketing and customer services, image recognition, autonomous vehicles, customized learning experiences, video games, and more.
AI engineers stay at the forefront of research developments to advise organizations on AI strategy. They may create entire AI processes and strategies, oversee data collection and processing, perform statistical analysis of the data, build and update models, and integrate results into the company’s processes, applications, and systems.
With the increasing scope and accuracy of models and recent developments in NLP and CV, having an AI engineer on your team is pertinent—if not essential—to keep your business competitive.
If you aren’t sure how AI fits into your business, hiring an experienced AI engineer in a consulting role can help you identify the systems that can be optimized with AI.
If you already have ML engineers and data scientists on staff, an AI expert can maximize the efficiency of ML model integrations, AI strategy, and processes.
Securing the right AI engineer to address company goals is vital to an organization’s present and future success. With the selection criteria and practical hiring tips covered in this guide, you’ll be prepared to select a new employee for various AI specializations.
The technical content presented in this article was reviewed by Matias Aiskovich.
Featured Toptal Artificial Intelligence Publications
