Abhimanyu is a machine learning expert with 15 years of experience creating predictive solutions for business and scientific applications. He’s a cross-functional technology leader, experienced in building teams and working with C-level executives. Abhimanyu has a proven technical background in computer science and software engineering with expertise in high-performance computing, big data, algorithms, databases, and distributed systems.
Dan is a software architect and technology professional focusing on applications of blockchain technologies. He has years of experience providing professional consulting services to clients ranging from startups to global corporations. He specializes in bringing rigorous testing and bulletproof code to tough engineering challenges. He has deep expertise in many aspects of artificial intelligence, blockchain, machine learning, and automation.
Russell builds data-driven products and data-driven teams. He has more than 15 years of experience inventing, rapidly prototyping, and deploying products driven by machine learning and natural language processing. Russell loves consulting on data science projects in the early stages.
Machine Learning engineers are experts in building, designing and optimizing artificial intelligence (AI) systems. This guide to hiring Machine Learning engineers features interview questions and answers, as well as best practices that will help you identify the best candidates for your company.
... 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.
Building a cross-platform app to be used worldwide
Creating an app for the game
Leading a digital transformation
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What our clients think
Clients Rate Toptal Machine Learning Engineers4.4 / 5.0on average across 434 reviews as of Sep 30, 2023
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 start up, 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
K Dunn & Associates
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
Site Specific Software Solutions
We used Toptal to hire a developer with extensive Amazon Web Services experience. We interviewed four candidates, one of which turned out to be a great fit for our requirements. The process was quick and effective.
Abner Guzmán Rivera, CTO and Chief Scientist
Sergio was an awesome developer to work with. Top notch, responsive, and got the work done efficiently.
Dennis Baldwin, Chief Technologist and Co-Founder
Working with Marcin is a joy. He is competent, professional, flexible, and extremely quick to understand what is required and how to implement it.
André Fischer, CTO
We needed a expert engineer who could start on our project immediately. Simanas exceeded our expectations with his work. Not having to interview and chase down an expert developer was an excellent time-saver and made everyone feel more comfortable with our choice to switch platforms to utilize a more robust language. Toptal made the process easy and convenient. Toptal is now the first place we look for expert-level help.
Derek Minor, Senior VP of Web Development
Networld Media Group
Toptal's developers and architects have been both very professional and easy to work with. The solution they produced was fairly priced and top quality, reducing our time to launch. Thanks again, Toptal.
Jeremy Wessels, CEO
We had a great experience with Toptal. They paired us with the perfect developer for our application and made the process very easy. It was also easy to extend beyond the initial time frame, and we were able to keep the same contractor throughout our project. We definitely recommend Toptal for finding high quality talent quickly and seamlessly.
Ryan Morrissey, CTO
Applied Business Technologies, LLC
I'm incredibly impressed with Toptal. Our developer communicates with me every day, and is a very powerful coder. He's a true professional and his work is just excellent. 5 stars for Toptal.
Pietro Casoar, CEO
Ronin Play Pty Ltd
Working with Toptal has been a great experience. Prior to using them, I had spent quite some time interviewing other freelancers and wasn't finding what I needed. After engaging with Toptal, they matched me up with the perfect developer in a matter of days. The developer I'm working with not only delivers quality code, but he also makes suggestions on things that I hadn't thought of. It's clear to me that Amaury knows what he is doing. Highly recommended!
George Cheng, CEO
As a Toptal qualified front-end developer, I also run my own consulting practice. When clients come to me for help filling key roles on their team, Toptal is the only place I feel comfortable recommending. Toptal's entire candidate pool is the best of the best. Toptal is the best value for money I've found in nearly half a decade of professional online work.
Ethan Brooks, CTO
Langlotz Patent & Trademark Works, Inc.
In Higgle's early days, we needed the best-in-class developers, at affordable rates, in a timely fashion. Toptal delivered!
Lara Aldag, CEO
Toptal makes finding a candidate extremely easy and gives you peace-of-mind that they have the skills to deliver. I would definitely recommend their services to anyone looking for highly-skilled developers.
Michael Gluckman, Data Manager
Toptal’s ability to rapidly match our project with the best developers was just superb. The developers have become part of our team, and I’m amazed at the level of professional commitment each of them has demonstrated. For those looking to work remotely with the best engineers, look no further than Toptal.
Laurent Alis, Founder
Toptal makes finding qualified engineers a breeze. We needed an experienced ASP.NET MVC architect to guide the development of our start-up app, and Toptal had three great candidates for us in less than a week. After making our selection, the engineer was online immediately and hit the ground running. It was so much faster and easier than having to discover and vet candidates ourselves.
Jeff Kelly, Co-Founder
We needed some short-term work in Scala, and Toptal found us a great developer within 24 hours. This simply would not have been possible via any other platform.
Franco Arda, Co-Founder
Toptal offers a no-compromise solution to businesses undergoing rapid development and scale. Every engineer we've contracted through Toptal has quickly integrated into our team and held their work to the highest standard of quality while maintaining blazing development speed.
Greg Kimball, Co-Founder
How to Hire Machine Learning Engineers through Toptal
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.
Work With Hand-Selected Talent
Within days, we'll introduce you to the right machine learning expert for your project. Average time to match is under 24 hours.
The Right Fit, Guaranteed
Work with your new machine learning 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.
How are Toptal machine learning engineers different?
At Toptal, we thoroughly screen our machine learning 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.
Our talent matchers are experts in the same fields they’re matching in—you’ll never deal with recruiters or HR reps. They’ll work with you to understand your goals, technical needs, and team dynamic and match you with ideal candidates from our vetted global talent network.
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 a machine learning engineer with Toptal, you’ll always work with world-class, custom-matched machine learning engineers ready to help you achieve your goals.
How quickly can you hire with Toptal?
Typically, you can hire a machine learning engineer with Toptal in about 48 hours. Our talent matchers are experts 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 dynamic and match you with ideal candidates from our vetted global talent network.
Once you select your machine learning 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.
What is the no-risk trial period for Toptal machine learning engineers?
We make sure that each engagement between you and your machine learning 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 a technology entrepreneur who strives to provide clients with end-to-end service when creating new software solutions or revamping old ones. Some of the projects she has completed include financial and accounting systems, ML-powered systems for NLP, forecasting, and anomaly detection. Tetyana has worked for clients in several countries and in various industries, such as energy, government, education, and biotechnology.
So how hard can it be to find an ML engineer? Well, not very hard at all if the goal is just to find someone who can legitimately list machine learning on their resume. But if the goal is to find an ML expert who has truly mastered its nuances, power, and strategic applications, then the challenge is most certainly formidable.
You will need to understand both your business needs and how ML may be used to implement their ideal solutions. You’ll then want to create a highly effective recruiting and evaluation process specifically geared toward finding not simply a qualified ML engineer, but the right ML engineer for your specific needs. Your first move in this process, however, is to read on and learn more about each of these critical steps.
What attributes distinguish quality Machine Learning Engineers from others?
Talented ML engineers not only are theoretically knowledgeable and technically proficient, but also own a variety of soft skills that enhance their ML-specific abilities.
Databases (both relational and not) and data warehousing solutions
Ability to understand and solve problems with minimal guidance from the business
Ability to question assumptions
Investigative mindset and data-driven argumentation
How can you identify the ideal type of Machine Learning Engineer for you?
You now know how to identify a quality ML engineer from a general standpoint. But ML problems can be quite varied, so you’ll need to identify your specific business needs in order to find the ideal ML engineer to address them. Start by drafting a “problem statement” to identify the issues you’re looking to solve, and how ML will be a part of the solution.
Your problem statement should include, at a minimum, the following considerations:
What business cases are you looking to improve?
Some business cases for ML considerations can be found here, with additional insights below.
Are you looking for a long or short-term engagement?
Do you have a well-defined requirement or are you looking for someone to help with the business process overall using ML?
Which areas of the business require the expertise of an ML engineer?
Who will be available to participate in the design/redesign of ML-empowered processes?
What are your existing/desired cloud/on-premises platforms?
What programming languages are used in your business?
What databases do you have or plan to use?
What level of automation does the project require?
Once you’ve addressed these questions in your problem statement, you can use the following guides to determine 1) whether your needs are best suited to a junior or senior-level ML engineer and 2) the particular candidate skill sets you should prioritize, based on your specific business cases:
Junior ML Engineers –
These engineers will be able to make decisions in the areas of data selection/preparation, model development, and technology implementation. They’ll also be expected to take guidance from your data scientists and DevOps engineers.
Senior ML Engineers –
By virtue of their more extensive backgrounds and longer histories in the space, quality senior ML engineers will likely be more advanced in the day-to-day functions noted above. However, they will also look beyond the day-to-day with a “big picture” mindset to identify the areas of your business that can be improved using ML. Senior engineers should be able to understand your business process and select the appropriate technology tools to integrate seamlessly with your existing infrastructure.
Priority Skills by Business Case
Forecasting – Look for an ML engineer who understands time series models. Sophisticated models such as Prophet or Long Short-term Memory (LSTM) offer good performance but sometimes hide the complexity of the underlying data. To ensure that your data is well-explored, look for an ML engineer who understands the basics of time series, i.e., seasonality, trend, autoregressive properties, and stationarity.
Customer segmentation – Look for an ML engineer who has knowledge of clustering algorithms, techniques for defining the number of clusters, and performance of clustering models. A good understanding of business metrics, such as customer satisfaction, purchase history, and customer lifetime value, is also important.
Fraud detection – Look for an ML engineer who has experience with anomaly detection models, unsupervised learning for detection of new fraud patterns, unbalanced classification and/or clustering, and understanding outliers, as well as effective application of ML metrics to maintain model relevance.
Identity verification, video monitoring, and/or automatic video and image labeling – Look for an ML engineer who understands that many systems require the examination of video streams, e.g., to identify intruders on a property, to assist in remote identity verification, to automatically classify movies and TV shows by genre, or to detect actors. These skills rely on image classification/segmentation techniques, which are often based on state-of-the-art deep learning architectures, so understanding them is essential. However, it is also important for an ML engineer to understand the intricacies of video stream processing, data compression, storage of large unstructured data, and performance of ML models trained on images.
Sound classification and voice generation – Look for an ML engineer who understands that sound is typically processed using the Fourier transform to create time/frequency “images” that can be processed in the same manner as images. Additionally, the right candidate will also possess the image classification/segmentation skills and experience noted in the identity verification skills description, above.
Text processing, chatbots, search engines, and text generation – Look for an ML engineer who has expertise in text tokenization, embedding, simple models (such as Multinomial Naive Bayes and Word2vec), and state-of-the-art models. In addition to modeling, experience with text storage and compression are also important.
How to Write a Machine Learning Engineer Job Description for Your Project
You’ve identified the experience level and skills you will require in your ML engineer. Now it’s time to find that perfect fit. Like most job postings, each job title will feature a similar/standard set of roles and responsibilities. To aid in this piece, consider referencing this ML engineer job posting template. Then, make sure to include explicit requirements as determined in your problem statement considerations to help candidates self-select before applying.
What are some key interview questions to ask Machine Learning Engineer job candidates?
As much as both interviewer and interviewee will often try to stick to a basic script, most good interviews will veer into conversational territories as each answer will often raise further unplanned, but related follow-up questions. That said, it’s important to have a list of questions that you know you’ll need answered to properly assess your candidate. Consider having these interview questions at the ready during your meeting, as well as the following:
What is the difference between deep learning and machine learning?
The main difference between deep learning (DL) and ML is that DL is a neural network (NN) architecture with multiple hidden layers. While conceptually this is not much different from a single-layer NN (an ML perceptron), the addition of hidden layers allows for the encoding of very complex relationships between features and a target variable. This, in turn, allows for efficient processing of large unstructured data, such as images, text, and audio.
What types of neural networks exist?
The field of artificial neural networks is constantly evolving and many types of NNs have been proposed, tested, and put into practice. Although different sources propose different taxonomies, the most common types of NNs in typical commercial applications include the perceptron, feed-forward NN, convolutional NN, and recurrent NN. Perceptrons are useful for creating basic models. Feed-forward NNs have applications in various fields and their advantage is the availability of different activation functions. Convolutional NNs are widely used in image-, video-, and sound-processing applications. Recurrent NNs are used in sequence processing, most notably in NLP. They are the basis of transformer neural networks.
How many of the top 10 machine learning algorithms can you name? How familiar are you with each? Please give examples.
Linear regression, logistic regression, K-means, random forest algorithm, SVM algorithm, decision tree, KNN algorithm, Naive Bayes algorithm, gradient and AdaBoost algorithm, dimensionality reduction algorithms.
Why do companies hire Machine Learning Engineers?
Machine learning engineers use ML to improve data processing and insight extraction. ML engineers share many responsibilities with data scientists; however, in addition to building models, ML engineers develop pipelines and maintain models in a production setting. Their typical workflow includes the following steps:
Best model selection: comparison of models, cross-validation, hyperparameter tuning
ML pipeline creation: set up of evaluation metrics, alert methods, and integration with business apps and dashboards
ML pipeline testing
ML pipeline deployment: docker, Terraform script, or similar
Performance tracking and maintenance: automated retraining, performance alerts, and experiments tracking
Model upgrades: usually when new types of data become available or business objectives are modified
Increasingly, machine learning is providing the solutions to many of our everyday issues, both personal and professional. As business leaders, integrating ML into as many aspects of our work as possible is not simply sufficient, it’s necessary to get and stay ahead of our competitors.
Developing a high-level understanding of ML and a proficiency in its many current and potential business applications will provide you with the critical abilities to identify ML use cases in your own company and hire the ideal ML engineer(s) to implement the right solutions.