Hire the Top 3% of Freelance Machine Learning Engineers
Toptal is a marketplace for top machine learning developers, engineers, programmers, coders, architects, and consultants. Top companies and startups can hire Toptal dedicated (full-time), hourly, or part-time machine learning freelancers 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.
Reza is a highly skilled machine learning engineer with 11 years of experience. Currently working at IBM, he has developed and deployed cutting-edge machine learning algorithms to enhance virtual assistant products. His research at the University of Toronto focused on numerical modeling techniques for solving multiphase flow problems. With a strong background as a machine learning engineer and data scientist, Reza's expertise lies in natural language processing and computer vision.
Dawid has delivered more than 30 successful projects in data science and machine learning. He has worked with both classical and deep learning solutions in a few industries. Dawid is focused on creating systems that follow MLOps best practices and design patterns. Having experience with many cloud providers, he is able to automate the whole ML process, from data gathering to automated deployments and continuous training.
Julio is a machine learning engineer, specializing in big data analytics. For the past three years, he has helped major companies, such as Fiat Chrysler Automobiles and a Brazilian credit bureau, improve their machine learning platforms. By combining big data and analytics, he has developed machine learning pipelines that run hundreds of machine learning models. In addition to his industry experience, Julio teaches university and other online courses in machine learning and data science.
Pedro is a software developer and architect specializing in data science, machine learning, and AI. He has extensive experience in the end-to-end process of conceiving, designing, developing, and deploying data applications for large companies and startups.
Timo is a full-stack data scientist with eight years of professional experience in data-heavy applications and a PhD in machine learning and statistics. He can work in different roles on the data lifecycle in industrial applications as a data engineer, data scientist, ML engineer, or data analyst. Timo is experienced with Python and SQL, and many modern data frameworks.
Michał has almost nine years of professional experience in data science, machine learning, and software development. He has a computer science background and can fit in data scientist and machine learning engineer roles. Michal has tackled multiple problems in conversational AI, NLP, computer vision, time series forecasting, social media analysis, learning from graph-structured data, supply chain analysis, data visualization, and production deployment.
Arshak has 3+ years of experience working as a data scientist and machine learning developer. He helped businesses become more profitable by increasing the click-through rate through recommendation systems and the retention rate of marketing campaigns using uplift modeling. Arshak is looking for projects that allow him to work with data to get valuable insights and develop machine learning algorithms to solve business tasks, showing his proficiency in Python, machine learning, and deep learning.
Ivan is a passionate machine learning engineer and full-stack software developer. His expertise includes machine learning and computer vision technologies, with proficiencies in Python and R in the data science field. Ivan holds a master's degree in computer science and is experienced in leading and managing development teams.
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.
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.
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Despite accelerating demand for coders, Toptal prides itself on almost Ivy League-level vetting.
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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 Machine Learning 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 machine learning expert for your project. Average time to match is under 24 hours.
3
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.
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 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. 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 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.
How do I hire a Machine Learning engineer?
To hire the right Machine Learning 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 Machine Learning engineers for your project.
Can you hire Machine Learning engineers on an hourly basis or for project-based tasks?
You can hire Machine Learning 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 Machine Learning engineers can fully integrate into your existing team for a seamless working experience.
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
Soft skills
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:
Identify problems
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?
Define stakeholders
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?
Define technologies
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?
Consider MLOps
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:
Experience Level
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:
Data collection: optional, can be performed by data engineers or data scientists, or can be taken from a ready data set
Exploratory data analysis (EDA)
Base model building
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
Conclusion
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.