Not only are machine learning engineer positions in high demand, with companies willing to pay top dollar for the right engineers, but responsibilities for these roles have greatly diversified. As you prepare for interviews, you are likely looking at all of these positions and figuring out where you fit.
In this article, by Jameson Merkow, Principal Software Engineer, Microsoft Health and Life Sciences — Medical Imaging AI, we provide a brief overview of different areas within machine learning engineering to help you sort through the required skills and responsibilities of these positions.
Jameson is an experienced leader in AI with over 15 years of experience in AI system design. He has spent over a decade working hands-on in computer vision and deep learning projects. Jameson has a passion for healthcare ML systems. He is proficient in multiple programming languages and frameworks and has a deep interest in managing, growing, and mentoring teams of software engineers.
Here’s what we’ll cover in this article:
- What Factors Should You Consider When Evaluating an Machine Learning Engineer Role?
- Machine Learning Engineering Roles: Product vs. Research
- Machine Learning Engineering Roles by Company Size
- Machine Learning Engineering Roles by Business Profile
- FAQs on Machine Learning Engineer Roles
Before we take a deep dive into the different roles ML Engineers play, let’s look at the basic definition.
What Is a Machine Learning Engineer?
A Machine Learning (ML) Engineer is a professional who is responsible for designing, developing, and deploying ML models and systems. Their main focus is on building and maintaining ML infrastructure, creating ML models, and integrating them into production systems.
Some of the key responsibilities of an ML Engineer include:
- Collecting and preprocessing data for training and testing ML models
- Building, training, and evaluating ML models using various algorithms and techniques
- Optimizing and fine-tuning ML models to improve their performance
- Deploying and maintaining ML models in production environments
- Working closely with data scientists and other members of the team to understand the business problem and design ML solutions
- Building and maintaining ML infrastructure, such as data pipelines, model serving, and monitoring systems
Recommended Reading: Machine Learning Engineer Salary in the US
What Factors Should You Consider When Evaluating an Machine Learning Engineer Role?
As you are looking for that new position in machine learning, you have probably noticed a wide variety of responsibilities with little or no correlation to the job title. What’s expected from, say, an ML Engineer can be completely different between Company A and Company B. Even at the same company, the same job title can have completely different responsibilities.
This inconsistency makes looking for the perfect role tricky, and you need to dive a little deeper into what makes these roles so different. When evaluating a role, team, or company:
- Determine if it is a product or research-focused role
- Investigate the team’s (or company’s) size and stage
- Learn as much as you can about their business profile
We’ll look at all the factors to consider under each of these parameters.
1. Machine Learning Engineering Roles: Product vs. Research
Let’s focus on the difference between a product- vs. a research-focused role.
ML Product Engineers
As the name suggests, ML product engineers work on actual products that are, in some way, sold to customers (people or businesses). These products must be useful to be sold.
ML products are goods or services that:
- Require ML components to operate (e.g., an autonomous vehicle)
- Enhance existing products (e.g., fraud detection in financial transactions)
- Or generate some business insight (e.g., improving ride-share matching to increase productivity)
Products are meant to be profitable, i.e., their value must be greater than their cost. There are productivity requirements (scale) and budgetary constraints to do this.
ML Research Scientists
Conversely, research scientists do not work on products directly. Instead, they work to expand the body of knowledge by discovering new techniques or answering novel questions.
Research scientists:
- Work to prove or disprove some hypothesis
- Share their results with others (often through publication or internal whitepapers)
- Work to find solutions to open-ended problems (often significantly so) without productivity or budgetary concerns
ML Infrastructure Engineer
Most roles fall on a spectrum between these two definitions. Most companies working in ML require constant technological growth. While a researcher would create any new technologies needed for building a new product, an ML product engineer would need to understand the technology well enough to productize it.
There is an inherent trade-off between these two sides of development, and a new position has arisen to reduce the barriers between research and product: ML infrastructure engineer. These engineers:
- Create platforms and tools to help product engineers perform research
- Help researchers commercialize their ideas into products
Thus, you should first find out which side of the product-to-research spectrum does the role you are applying to fall.
Recommended Reading: Machine Learning Engineering Interviews — What to Expect From System Design Rounds
2. Machine Learning Engineering Roles by Company Size
Another major factor in determining a role's responsibilities is the size or stage of the company itself.
Machine Learning Engineering at FAANG+ or Well-funded Startups
Working for larger companies like FAANG+ or well-funded startups has several advantages:
- Brand recognition and higher pay scales
- Roles’ responsibilities tend to be narrower, clearly defined, and more specialized
Hence, you might need a smaller breadth or in-depth knowledge for the role you are applying for. The downside is that these advantages attract a lot of applicants, making the competition extremely high.
The interviews tend to be more challenging with harsher evaluations since these companies can afford to (and often need to) filter out a large number of applicants. The good news is that conducting many interviews has made the interview loops more predictable, making them easier to prepare for.
Machine Learning Engineering at Newer Startups
On the other side of the spectrum are newer startups. Building ML products and services require a wide variety of skills. In large companies, these skills are covered by multiple specialized personnel. However, startups do not have the budget to hire one engineer for each role, so they search for candidates with a well-rounded, diverse skill-set.
Most startups attract only a fraction of the candidates for a given job posting compared to large corporations. This constraint leads to a more thorough and personalized interview for candidates who apply.
The advantage is that this process tends to be more forgiving, and startups evaluate you holistically, with fewer required qualifications. However, startups typically have a less polished interview process with arbitrary evaluation, making these interviews harder to prepare for.
Expect Exceptions!
As expected, there are exceptions to these rules. For example:
- Many large companies have smaller teams that operate similar to a startup
- Or startups may have built well-defined loops, especially those started by industry veterans
Many companies have yet to construct a consistent pipeline in the face of the rapidly growing ML industry. Thus, identifying the stage of the company becomes vital in your journey to prepare for a position at it.
Recommended Reading: Machine Learning Engineer Resume Guide: Tips, Best Formats, and Sample Included
3. Machine Learning Engineering Roles by Business Profile
Another attribute to consider when preparing for interviews is the business profile of the company or team. A business profile includes two parts:
- What you are selling (the product profile)
- Who you are selling to (the customer profile)
Vertical vs. Horizontal Product Profiles
In product profiles, there are two main categories — verticals vs. horizontals:
- Verticals are applications or services that solve a particular problem for the customer
- Horizontal is a tool that helps a company build its applications
For example, Amazon Web Services (AWS) almost exclusively sells tools to help others develop their products. Conversely, Netflix sells an entertainment service (which uses AWS tooling).
Vertical-based roles require a great depth of domain knowledge (within that vertical). Interviews for vertical roles will include an in-depth discussion of that domain (for example, healthcare AI or self-driving cars).
Horizontal-based roles involve a lot of different disciplines, and interviews for these positions will concentrate on a lot of “what-if” scenarios.
Customer Profile — B2B vs. B2C
When considering the customer profile, ask yourself:
- Is this a consumer-facing company or B2B?
- Do they sell ML products to consumers or sell products to other businesses?
Consumer products use marketing to sell their products to individuals. Such teams do not have any engineering staff that interacts with customers. On the other hand, enterprise (or B2B) products are sold individually to other businesses, often tailoring the product to each new business, requiring a solution architect/engineer to interact with customers and modify or enhance the product for their use case.
Each of these profiles has different requirements for its engineers/scientists, which leads to varying priorities during interviews. Hence, you should identify these profiles and tailor your preparation accordingly.
However, even with varying requirements of the role and different approaches taken by companies, there are still some common interview scenarios that you should be aware of. All the interviews for an ML engineer position would consist of some combination of these rounds, which we will cover in a separate article.
Recommended Reading: 50+ Machine Learning Interview Questions
FAQs on Machine Learning Engineer Roles
1. What are machine learning engineer roles and responsibilities?
The role of a machine learning engineer typically involves the design, development, and deployment of machine learning models. Specific responsibilities may include:
- Collecting and preprocessing large datasets for use in machine learning models.
- Building, testing, and evaluating various machine learning models using techniques such as supervised and unsupervised learning.
- Selecting the most appropriate algorithms and tools for specific projects.
- Tuning and optimizing the performance of machine learning models.
- Deploying machine learning models in production environments.
- Monitoring and maintaining deployed models to ensure they continue to perform well.
- Collaborating with data scientists, software engineers, and other stakeholders to implement machine learning solutions.
- Staying current with the latest advancements in machine learning and artificial intelligence.
2. What are the skills required for machine learning engineer?
The skills required for a machine learning (ML) engineer vary depending on the specific job and the industry, but generally include:
- Strong programming skills: ML engineers need to be proficient in at least one programming language, such as Python or R, and have experience with ML libraries and frameworks like Tensorflow, PyTorch, and scikit-learn.
- Strong mathematical and statistical background: ML engineers need to have a strong understanding of mathematical and statistical concepts, such as linear algebra, probability, and statistics, as well as experience with implementing these concepts in practice.
- Experience with data preprocessing and cleaning: ML engineers need to have experience with cleaning and preparing data for analysis, including handling missing values, normalizing data, and dealing with outliers.
- Experience with machine learning algorithms: ML engineers need to have experience with a variety of ML algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, as well as the ability to select the most appropriate algorithm for a given problem.
- Experience with Deep Learning: ML engineers should have experience with deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks.
- Experience with cloud platforms: ML engineers should have experience with cloud platforms like AWS, Azure and GCP, as well as experience with deploying ML models in production environments.
- Experience with DevOps practices: ML engineers should have experience with containerization, and experience with creating and deploying ML models on production environments.
- Good communication and teamwork: ML engineers need to be able to communicate effectively with other members of the team, such as data scientists, software engineers, and business stakeholders.
- Continual learning: Machine learning is a rapidly evolving field, and ML engineers need to be able to continuously learn and adapt to new technologies and algorithms.
3. What are the job title for machine learning engineer?
The job title for a machine learning engineer can vary depending on the company and industry, but some common job titles include:
- Machine Learning Engineer
- Data Scientist
- Deep Learning Engineer
- Artificial Intelligence Engineer
- Machine Learning Developer
- Artificial Intelligence Developer
- Machine Learning Scientist
- Data Engineer
- Computer Vision Engineer
- Natural Language Processing Engineer
- Predictive Modeling Engineer
- Machine Learning Researcher
- Machine Learning Analyst
4. What are the roles in ML projects?
In a machine learning (ML) project, there are several roles that are typically involved, including:
- Data Scientist: responsible for designing and implementing the ML model, as well as analyzing and interpreting the results. They also responsible for feature engineering and data pre-processing.
- Machine Learning Engineer: responsible for implementing the data scientist's design and deploying the model into production. They also responsible for scaling and maintaining the model.
- Data Engineer: responsible for collecting, cleaning, and preparing the data for use in the ML model. They also responsible for building and maintaining the data pipeline.
- Project Manager: responsible for managing the project, including setting timelines and budgets, communicating with stakeholders, and ensuring that the project is delivered on time and within budget.
- Business analyst: responsible for identifying the business problem, and translating it into a ML problem and validating the performance of the model.
- DevOps Engineer: responsible for automating the deployment and scaling of the model in production.
- Quality Engineer: responsible for testing and validating the model performance and stability.
5. Is SQL necessary for machine learning engineer?
SQL (Structured Query Language) is not strictly necessary for a machine learning (ML) engineer, but it can be a useful skill to have.
SQL is used to access and manipulate relational databases, and many organizations store large amounts of data in relational databases. As a result, a ML engineer may need to use SQL to extract the data from databases and prepare it for analysis.
In addition, SQL can be useful for:
- Creating and manipulating tables and views
- Joining tables and perform complex queries
- Performing data cleaning and data preprocessing
- Storing intermediate results and model results in a database
- Accessing specific data for debugging and testing purposes
In summary, while SQL is not strictly necessary for an ML engineer, it can be a useful tool to have in the toolbox when working with data stored in relational databases. Other skills like data wrangling with pandas, data visualization and data exploration with visualization libraries like matplotlib, seaborn and plotly are also important for a ML engineer to have.
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