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.
United StatesToptal Member Since December 20, 2015
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.
United StatesToptal Member Since December 13, 2016
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.
Necati is a computer scientist with 17 years of experience in the private industry, focusing on DevOps and machine learning. He is also an AWS Certified Solutions Architect with a PhD in computer engineering. He has led teams and driven infrastructure and architecture decisions for the last 10 years. Necati also takes an active role in the implementation and design phases of the infrastructure, architecture, and process.
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
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K Dunn & Associates
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Site Specific Software Solutions
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Applied Business Technologies, LLC
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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 engineer 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.
How are Toptal machine learning engineers different?
At Toptal, we thoroughly screen our machine learning engineers to ensure we only match you with talent of the highest caliber. Of the more than 200,000 people who apply to join the Toptal network each year, fewer than 3% make the cut. You'll work with engineering experts (never generalized recruiters or HR reps) to understand your goals, technical needs, and team dynamics. The end result: expert vetted talent from our network, custom matched to fit your business needs. Start now.
Can I hire machine learning engineers in less than 48 hours through Toptal?
Depending on availability and how fast you can progress, you could start working with a machine learning engineer within 48 hours of signing up. Start now.
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. Start now.
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 should you craft your machine learning engineer job posting?
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:
Q: What is the difference between deep learning and machine learning?
A: 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.
Q: What types of neural networks exist?
A: 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.
Q: How many of the top 10 machine learning algorithms can you name? How familiar are you with each? Please give examples.
A: 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.