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Hiring Machine Learning Engineers - a primer

Machine Learning has gained significant attention in recent years due to the explosion of data produced by applications, the increase in computational power, and the development of more advanced algorithms. It is used in a wide range of applications, from automating mundane tasks to providing intelligent insights, and industries in all sectors are trying to capitalize on its potential.

Are you tasked with recruiting Machine Learning (ML) engineers but have no idea where to start? Worry not, our step by step guide will give you all the information and knowledge you need to get started!

What is Machine Learning?

Let's start by understanding what Machine Learning (ML) really is.

Machine learning is a field of artificial intelligence that involves training computers to perform tasks without explicit programming.

It allows machines to learn and adapt independently by analyzing and identifying patterns in data. This is done using algorithms, which are sets of rules or procedures that a computer follows to solve a problem.

The idea behind machine learning dates back to the 1950s, but it wasn't until the 1990s that the technology and computational power needed to make it a practical reality became available. Since then, the field has exploded in popularity and is now used in a wide range of applications.

One way machine learning is used is in image and speech recognition. For example, a machine learning model might be trained to recognize different types of animals in images. If it is then shown a new image, it can use what it has learned to classify the animal in the image. Similarly, a machine learning model might be used to analyze speech patterns to identify different languages or accents.

Another common application of machine learning is in prediction. For example, a machine learning model might be used to analyze data on past housing prices to predict future prices. Or, it might be used to analyze customer data to predict which products a particular customer is likely to purchase.

There are many other uses for machine learning as well. It can be used to classify emails as spam or non-spam, to diagnose medical conditions, to recommend products to customers based on their past purchases, and much more.

Incredible isn't it?

Why is Machine Learning Important?

Machine Learning (ML) has become an increasingly important field in recent years, and for good reason.

It allows computers to perform tasks without explicit programming and enables them to learn and adapt independently. This has opened up a whole world of possibilities in various industries and applications.

One of the main benefits of ML is its ability to analyze and process vast amounts of data. In today's digital world, we generate an incredible amount of data, and ML helps us make sense of it all. It can identify patterns and trends in data that would be impossible for humans to detect, and it can do so quickly and accurately. This allows organizations to gain valuable insights and make better informed decisions.

ML is also being used to automate a wide range of tasks, from identifying spam emails to diagnosing medical conditions. This not only saves time and resources, but it can also lead to more accurate and efficient processes. In some cases, ML is even being used to replace humans in certain tasks, such as operating machinery or analyzing financial data.

Another important aspect of ML is its ability to improve over time.

As it is fed more data, an ML model can continually learn and adapt, leading to even better performance. This makes it a powerful tool for companies looking to stay competitive and innovative.

What is the difference between Machine Learning, AI, NLP and Deep Learning?

The terms AI, NLP and Deep Learning are often thrown around casually alongside Machine Learning (ML). Before diving in to know the differences, let's quickly review these terms. Broadly speaking:

  • Artificial Intelligence (AI) is the broad discipline of creating intelligent machines. This is the overall area that encompasses all of these technologies including Machine Learning.
  • Machine Learning (ML) - refers to systems that can learn from experience and is the focus of this blog.
  • Natural Language Processing (NLP) - refers to systems that can understand human spoken and written languages.

Now let’s view them in context of a Venn Diagram:


AI (Artificial Intelligence)

As we can see AI is the overarching discipline that covers anything related to making machines smart. Whether it’s a robot, a refrigerator, a car, or a software application, if you are making them smart, then it’s AI. 

ML (Machine Learning)

Machine Learning (ML) is commonly used alongside AI but they are not the same thing. ML is a subset of AI. ML refers to systems that can learn by themselves. Systems that get smarter and smarter over time without human intervention. 

NLP (Natural Language Processing) 

Natural Language Processing (NLP) is also a subset of Artificial Intelligence (AI) that enables machines to understand human languages. Its goal is to build systems that can make sense of text and speech, and automatically perform tasks like translation, spell check, or topic classification. NLP is used to redefine how machines understand human languages and behavior.

Deep Learning

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.

Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before. A popular example of a real world application of Deep Learning is the autonomous driving taxi Waymo which began in 2009 by Google and now is a common sight on the streets of San Francisco. 

Traditional Machine Learning vs. Deep Learning

Why hire a Machine Learning engineer versus using third party tools?

As a founder of a startup, you are likely always looking for ways to give your company a competitive edge. One way to do this is by leveraging the power of machine learning (ML). Hiring an ML engineer can be a great way to incorporate this technology into your business, but it's important to understand the advantages and disadvantages of this approach before making a decision.

One of the main advantages of hiring an ML engineer is the ability to tailor the technology to your specific business needs. An ML engineer can work with you to understand your business goals and design and build custom ML models that can help you achieve those goals. This can be especially valuable if you have unique or complex data or requirements.

Another advantage of hiring an ML engineer is the ability to control the development process. With an in-house team, you have more control over the timeline, budget, and direction of your ML projects. This can be especially important if you need to iterate quickly or make changes to your models as your business evolves.

However, hiring an ML engineer is not without its drawbacks. One of the main disadvantages is the cost. ML engineers are in high demand and can command high salaries, which may not be feasible for every startup. Additionally, building and maintaining custom ML models can be time-consuming and require ongoing resources and support.

Alternatively, you could consider using tools built by third-party companies like OpenAI. This can be a more cost-effective option, especially if you don't have the resources or need for custom models. It can also be faster, as you can get up and running with pre-built models quickly.

However, using third-party tools also has its drawbacks. One of the main disadvantages is the lack of control. You are limited to the models and features that the company provides, and you may not be able to customize them to your specific needs. Additionally, you may have to pay ongoing fees or share your data with the company, which can be a concern for some startups.

In conclusion, whether to hire an ML engineer or use third-party tools will depend on your specific business needs and resources. Hiring an ML engineer can give you more control and the ability to tailor the technology to your business, but it can be more expensive and time-consuming. Using third-party tools can be faster and more cost-effective, but you may have less control and customization options. Ultimately, the best choice for your startup will depend on your specific needs and goals.

What does a Machine Learning (ML) engineer do?

Machine learning engineers have two key roles: feeding data into machine learning models and then deploying those ML models. These tasks can be further broken down into four key areas:

  1. Data preprocessing: ML engineers often start by collecting, cleaning, and preprocessing data. This may involve tasks such as merging multiple data sources, handling missing values, and normalizing data.
  2. Model training: Once the data has been prepared, ML engineers use machine learning algorithms and libraries such as scikit-learn, TensorFlow, or PyTorch to train models on the data. This may involve tuning hyperparameters, selecting the appropriate evaluation metric, and evaluating the model's performance.
  3. Model deployment: After the model has been trained, ML engineers deploy it to a production environment. This may involve tasks such as integrating the model into an application, setting up an automated pipeline for model updates, and monitoring the model's performance in production.
  4. Model maintenance: ML engineers also often handle tasks related to maintaining and improving the performance of deployed models. This may involve tasks such as retraining models as new data becomes available, monitoring the model's performance over time, and identifying and fixing any issues that arise.

In addition to these tasks, ML engineers may also be involved in research and development activities, such as exploring new machine learning techniques and tools, and collaborating with data scientists and other team members to identify and solve problems.

Unlike traditional developers, ML engineers may spend just as much time fine tuning models and connecting them to each other versus writing code.

What tools and technologies do Machine Learning engineers use?

Now that we know the importance of hiring a Machine Learning (ML) Engineer in your team, let’s look at some of the tools and technologies they use:

  1. Programming languages: Machine learning engineers typically use programming languages such as Python, R, C++ and Java to build and implement machine learning models.
  2. Machine learning libraries and frameworks: There are many open-source libraries and frameworks that machine learning engineers use to build and train machine learning models, including TensorFlow, PyTorch, scikit-learn, and Keras.
  3. Data storage and manipulation tools: Machine learning engineers often work with large datasets and use tools such as SQL databases and big data processing frameworks (e.g., Hadoop, Spark) to store, manipulate, and extract insights from data.
  4. Development environments: Machine learning engineers often use integrated development environments (IDEs) such as PyCharm or Visual Studio to write, test, and debug code.
  5. Version control systems: Machine learning engineers use version control systems (e.g., Git, Mercurial) to track and manage changes to their codebase.
  6. Model evaluation and visualization tools: Machine learning engineers use tools such as TensorBoard, Matplotlib, and Seaborn to visualize and evaluate the performance of machine learning models.
  7. Cloud services: Machine learning engineers may use cloud-based platforms and tools, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, to build, train, and deploy machine learning models at scale.
If a machine learning engineer is using pre-trained models or tools from companies like OpenAI or Google and Microsoft, they may not need to use all of the above tools and technologies.

For example, they may be able to use a pre-trained model or a tool like Google's AutoML to build and train their own machine learning models without having to code from scratch. However, they may still need to use some of the above tools and technologies, such as programming languages and data storage and manipulation tools, to pre-process and manipulate their data.

What are common titles for Machine Learning (ML) engineers?

Some common titles for machine learning engineers include:

  1. Machine learning engineer
  2. Data scientist
  3. Artificial intelligence engineer
  4. Deep learning engineer
  5. Software engineer (with a focus on machine learning)
  6. Data engineer (with a focus on machine learning)

Keep in mind that the specific title that an individual holds may depend on their employer, the projects they are working on, and their level of experience and expertise in the field. Some companies may use different titles to refer to employees with similar skills and responsibilities.

Who are the top companies hiring Machine Learning (ML) engineers?

Some top companies that hire machine learning engineers include:

  1. Google: Google hires machine learning engineers to work on a variety of projects, including search, maps, and advertising.
  2. Microsoft: Microsoft hires machine learning engineers to work on projects related to cloud computing and general AI development.
  3. Amazon: Amazon hires machine learning engineers to work on projects related to e-commerce, retail, and logistics.
  4. Apple: Apple hires machine learning engineers to work on projects related to artificial intelligence, fraud detection and computer vision.
  5. Facebook: Facebook hires machine learning engineers to work on projects related to social networking, advertising, and virtual reality.
  6. Intel: Intel hires machine learning engineers to work on projects related to chip design, data analytics and graphics.
  7. NVIDIA: NVIDIA hires machine learning engineers to work on projects related to computer graphics and chip design and verification.
  8. IBM: IBM hires machine learning engineers to work on projects related to core AI and business applications of machine learning.
  9. Uber: Uber hires machine learning engineers to work on projects related to ride-sharing, food delivery, and transportation.
  10. Airbnb: Airbnb hires machine learning engineers to work on projects related to hospitality, travel, and real estate.

How to search for Machine Learning engineers?

There are only a few sources for hiring Machine Learning (ML) engineers - LinkedIn, Github or StackOverflow. Most of the mid tier job boards such as Indeed or Monster won't have access to this top level talent and is likely to be a waste of time. Niche job boards like HackerRank and Kaggle can also be very helpful.

Using titles:

One strategy is to focus on folks with specific ML related titles. While this search will be narrow it will produce very targeted results. An example boolean to use in the title includes:

("ML" OR "machine learning" OR "deep learning" OR "AI" OR "artificial intelligence" OR "NLP" OR "natural language") AND ("software" OR "engineer" OR "SDE" OR "programmer" OR "developer")

LinkedIn: Title based Machine Learning (ML) engineer search

This search produces 76k results globally. If we wanted to expand this search, other titles to include could be those related to computer vision:

Boolean: (CV OR "computer vision" OR "ML" OR "machine learning" OR "deep learning" OR "AI" OR "artificial intelligence" OR "NLP" OR "natural language") AND ("software" OR "engineer" OR "SDE" OR "programmer" OR "developer")

This increases the results to 82k but keep in mind that not all Computer Vision engineers may be a good fit for Machine Learning roles.

Using skills:

Another strategy is to focus on skills and use generic software engineering titles. The goal behind this search is that may folks may not have specific ML related titles but may still be working on ML. Here is an example boolean search:

Title: ("software") AND ("engineer" OR "Developer")

Keywords: ("machine learning" OR "artificial intelligence" OR "ML" OR "AI") AND ("Python" OR "R" OR "Java" OR "C++") AND ("TensorFlow" OR "PyTorch" OR "scikit-learn" OR "Keras")

This search produces another 57k results that are in addition to the first search above!

LinkedIn: Keyword based search for ML engineers

Sample Interview Questions for Machine Learning engineers

Typical technical interviews for Machine Learning Engineers span both hard and soft skills. Here are some sample questions for each of them based on our experience at Rocket.

Hard Skills

Logic & Algorithms
  1. What are Different Types of Machine Learning algorithms?
  2. What are the different types of Machine Learning? Explain each of them and their applications?
  3. What is deep learning, and how does it contrast with other machine learning algorithms?
  4. Explain Correlation and Covariance with their formulas?
  5. Explain SVM Algorithm in Detail
  6. What are Support Vectors in SVM?
  7. What are Loss Function and Cost Functions? Explain the key Difference Between them?
  8. What is ‘Naive’ in a Naive Bayes?
  9. What is PCA? When do you use it?
  10. Difference Between Sigmoid and Softmax functions?
  11. What is a Confusion Matrix? 
  1. How Do You Design an Email Spam Filter?
  2. Explain the Difference Between Classification and Regression?
  3. How to Tackle Over-fitting and Under-fitting?
  4. What is a Neural Network?
  5. How do you make sure which Machine Learning Algorithm to use?
  6. What is Collaborative Filtering? And Content-Based Filtering?
  7. What models do you train for fun, and what GPU/hardware do you use?
Programming Languages & Tools
  1. How do you handle missing or corrupted data in a dataset?
  2. Given two strings, A and B, of the same length n, find whether it is possible to cut both strings at a common point such that the first part of A and the second part of B form a palindrome. Use Python/Java/C++ to for writing the code
  3. Pick an algorithm. Write the pseudocode for a parallel implementation
  4. What is F1 score? How would you use it?
  5. How do you check the Normality of a dataset?
  6. Describe a hash table
  7. Which data visualization libraries do you use? What are your thoughts on the best data visualization tools?

Behavioral Skills / Soft Skills

  1. What are some examples of deployed Machine Learning projects?
  2. How do you think Google is training data for self-driving cars?
  3. How would you explain Machine Learning to a school-going kid?
  4. What do you think is the most valuable data in our business? 
  5. How would you implement a recommendation system for our company’s users?
  6. How can we use your machine learning skills to generate revenue?
  7. Where do you usually source datasets?
  8. How do you think quantum computing will affect machine learning?


Sourcing and hiring Machine Learning (ML) engineers is difficult but not unsurmountable. In summary:

  1. Look for a strong foundation in mathematics and statistics: Machine learning engineers should have a strong foundation in mathematics and statistics, as these subjects are central to the field of machine learning. Look for candidates who have a solid understanding of concepts such as probability, linear algebra, and optimization.
  2. Look for experience with programming languages and machine learning frameworks: Machine learning engineers should have experience with programming languages such as Python, R, and Java, as well as machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Look for candidates who have a track record of building and implementing machine learning models using these tools.
  3. Look for experience with data manipulation and storage tools: Machine learning engineers should have experience with tools such as SQL databases and big data processing frameworks (e.g., Hadoop, Spark) to store, manipulate, and extract insights from data. Look for candidates who have a strong understanding of data management and are able to work with large datasets.

About Rocket

Rocket pairs talented recruiters with advanced AI to help companies hit their hiring goals and knows technology recruiting inside out. Rocket is headquartered in the heart of Silicon Valley but has recruiters all over the US & Canada serving the needs of our growing client base across engineering, product management, data science and more.

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