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Top 10 Expert Machine Learning Engineers to Hire in 2023
| ML and Data Science Developer
Anton is a data scientist with 10 years of experience in software development. He has been a team lead on a number of projects.
| Web Developer
Victoria is a full-stack web developer with over 5 years of experience. She has a strong background in APIs and mainstream databases.
| Flutter Developer
Bakhrom has 5 years of experience as a mobile app developer. He is an expert in Flutter, Web3, FFmpeg, and computer vision technologies.
| ML, Data Science and Python Developer
Anton has 12 years of experience in high-load system development and 7 years of development experience in artificial intelligence, neural networks, and computer vision.
| ML and Data Science Developer
Danil is a data scientist with 3 years of experience in machine learning.
| ML and Data Science Developer
Kirill is a data scientist with 3 years of experience in machine learning.
| ML and Data Science Developer
Daniil is a data scientist with 3 years of experience in machine learning.
| Full-stack Web Developer
More than 4 years as a PHP developer; Full-stack Web Developer with a passion for the PHP, Python (Django), React.js. Solid experience in front-end, back-end, and...
| Web / Mobile Developer
Max has 9 years of experience in web & mobile development. He is committed to achieving the best results to ensure his projects are first-class.
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How to Interview and Hire Machine Learning Developers
Machine learning (ML), an important branch of artificial intelligence is making a big difference globally. Experts believe that ML will “drive economic growth in every industry”.
Since machine learning systems “learn” from huge data sets to improve the decision-making process, ML has many use cases. You can also use it along with other artificial intelligence (AI) capabilities like natural language processing (NLP), speech recognition, computer vision, image recognition, image processing, etc.
Many industries like banking, financial services, manufacturing, healthcare, etc. have important ML uses cases. You can transform many functions like marketing, customer service, information security, etc. with the help of ML.
Different types of organizations are already using ML to augment the functioning of their existing systems. They might have already used automation for simple tasks. They now manage more complex tasks with the help of the data-driven decision-making capabilities of ML.
ML makes a big difference to data science projects too. Data scientists use ML algorithms to process vast data sets containing unstructured data.
All of these factors make ML engineers highly sought-after in the job market! ML engineers can earn a handsome salary. You might find it hard to hire ML engineers due to the soaring demand.
The good news is that you have options at hand to hire smart ML engineers. You need to choose the right hiring platform.
Do you plan to hire ML engineers for a startup or enterprise? You have a great option, which is to hire from a software development company like DevTeam.Space.
Before we analyze the pros and cons of each type of platform, we first review the skill requirements of machine learning engineer jobs.
Primary skills required in a machine learning engineer role
You should look for ML engineers with a bachelor’s degree or master’s degree in computer science, computer engineering, information technology, or related fields. An ML engineer might need several of these primary machine learning skills:
- Proficiency in any one of the programming languages like Python, Julia, and Java;
- Good knowledge of useful ML development libraries offered by the leading programming languages, e.g., Numpy or Scikit-learn in Python;
- Thorough understanding of types of ML algorithms, e.g., supervised and unsupervised learning, etc.;
- Deep knowledge of predictive algorithms;
- Thorough knowledge of machine learning algorithms like Naïve Bayes classifier algorithm, K-means clustering algorithms, Support Vector Machine (SVM) algorithm, Linear Regression, Logistic Regression, Artificial Neural Networks (ANNs), Decision Trees, Random Forests, Nearest Neighbors, Dimensionality Reduction, Gradient Boosting, Principal Component Analysis (PCA), etc.;
- Sound knowledge of linear algebra;
- Familiarity with tools and techniques to process unstructured data;
- Good knowledge of preparing data sets from raw data, e.g., data cleaning, data analysis, etc.;
- Experience with AI models;
- Knowledge of ML model deployment processes;
- Good knowledge of ML and AI development platforms like Microsoft Azure Machine Learning Platform, Google AI Platform, etc.
Note: ML engineers must have the above skills. If you need senior-level ML engineers, then the skill requirements are larger.
Advanced skills required in senior-level machine learning engineering jobs
Experienced ML developers need the following skills:
- Sound knowledge of one or more of the AI capabilities like deep learning, neural networks, computer vision, image recognition, speech recognition, natural language processing, etc.;
- Experience in the data science field;
- The working knowledge of creating data science models;
- Data modeling skills;
- Proficiency with machine learning frameworks;
- In-depth understanding of statistical analysis;
- Sound knowledge of big data technologies like Hadoop, Apache Spark, etc.;
- Familiarity with deep learning models;
- Understanding of how to deploy deep learning models;
- Software development experience using programming algorithms offered by the relevant languages;
- Data engineering skills;
- Experience in developing predictive algorithms;
- Familiarity with tools used by data scientists and data analysts;
- Knowledge of how to design self-learning software systems;
- Experience in developing a machine learning algorithm;
- Knowledge of tuning machine learning models;
- Knowledge of the methodologies and practices in data science teams;
- Understanding of how data science teams use ML algorithms and models.
Senior ML developers should have an understanding of how data engineers work. They might need knowledge of programming languages like R that are used in data science projects.
Other software engineering skills required by machine learning engineers
You need smart ML developers to have the following software engineering skills:
- Sound knowledge of software engineering;
- Sufficient familiarity with software development lifecycle (SDLC);
- Good understanding of software development methodologies like waterfall and agile;
- Extensive experience in computer architecture;
- Knowledge of ensuring that software performs in line with the non-functional requirements (NFRs) like performance, scalability, etc.;
- Experience in developing secure applications by using tools like encryption;
- API development skills;
- Familiarity with human-centered design;
- Extensive code review experience;
- Deep knowledge of defect management and software quality management;
- Experience in using the leading version-control tools;
- Knowledge of how software engineers can use cloud computing to their advantage;
- Familiarity with planning and executing tests;
- Sufficient experience with DevOps processes, methods, tools, and practices.
Note: Larger data science teams and ML development teams use cloud computing platforms to meet their computing power requirements. Software engineers in ML or data science projects also use the DevOps tools offered by the cloud platforms. These make the knowledge of cloud computing crucial.
Competencies required in machine learning engineer jobs
In order to have a successful machine learning career, ML engineers need the following competencies:
- Customer focus;
- The ability to see the larger perspective;
- Urge to excel;
- Communication skills;
- Problem-solving skills;
- Collaboration skills;
How to find the top-ranked machine learning engineers?
Now that you know about the skills required in the machine learning developer career path, start the hiring process. Take the following steps:
1. Choose the right platform to hire ML engineers
ML is closely related to AI, furthermore, data scientists routinely use it. AI solutions like virtual assistants, AI-based trading, AI-powered risk assessment, etc. make considerable use of ML. Machine learning engineers design crucial solutions for data science prototypes too. We highlight all of these to point out the importance of ML. You probably have a strategic project at hand if you need to use ML.
The right hiring platform makes a big difference in important projects. You might think that you can get ML engineers at a low hourly rate on freelance platforms. We don’t recommend this approach though.
Freelance developers work part-time on your project. You might not get enough contributions from them. Managing freelancers in different time zones can be hard, furthermore, freelance platforms don’t offer any project management support.
Freelancers might leave your project mid-way. You need to spend plenty of time and effort hiring replacement developers.
We recommend you hire full-time ML engineers from a trustworthy hybrid software development company like DevTeam.Space. We have highly skilled and experienced ML developers.
In addition to our robust vetting process, we routinely encourage our developers to upskill. Therefore, our developers are motivated.
We provide management support. If you require, we can provide a cohesive ML development team.
2. Interview the candidates
After you select a hiring platform, you need to post your job there. Subsequently, interview the applicants.
Interviewing an ML engineer might involve multiple technologies like Python, SQL, Hadoop, etc. Do you need interview questions? Use our comprehensive interview questions. Check out the following examples:
Tailor your interview process to assess the relevant skills and experience. E.g., evaluate the experience of candidates on key aspects like data structures, data processing, ML model performance, data distribution, etc. Don’t confine the interview to theoretical questions only.
Job responsibilities of machine learning engineers include collaborating with stakeholders like customers, project managers, architects, testers, DevOps engineers, etc. Assess the experience of candidates in complex project environments.
Describe your project requirements. Ask how the candidates will approach your project. You should expect to hear specific answers.
3. Onboard ML engineers effectively
Assuming that you found and hired competent ML developers, it’s time for effective onboarding. Focus on making the onboarding process lean and efficient. This helps ML engineers to become productive quickly.
Provide the project documents to the new developers. Explain the project requirements to them. You might need to explain the technical solutions and software architecture.
Provide access to the project’s technical environment. The new ML developers need access to the code repository and other relevant tools.
You might already have existing software development teams, e.g., web development teams. Introduce the new developers to your existing team. Explain the roles and responsibilities of the team members.
Describe the project plan, furthermore, explain the project milestones. Establish a communications process with the new developers. You need to describe the work approval process. Finally, you need to establish accountability.
Tips for interviewing ML developers
The following interviewing tips could help you:
A. Look for the right balance of experience
Machine learning projects can be complex. Hire sufficiently experienced developers. You need the right mix of senior and mid-level developers in your team.
B. Evaluate the experience of implementing ML algorithms
ML algorithms are numerous. You need to implement relevant algorithms. ML engineers with experience in ML libraries in programming languages like Python can implement various algorithms easily. Assess their skills in this regard.
C. Look for developers with relevant experience
ML is a vast and evolving branch of AI. New innovations emerge routinely. Even the most experienced ML engineers might not know about all corners of this vast field. Look for developers with the specific capabilities that you need.
Samples of machine learning engineering questions
Ask questions to evaluate relevant and hands-on skills. Check out these examples:
A. Describe how you used cloud computing to make ML development more effective
This is an open-ended question. Expect candidates to describe how they used cloud computing to meet computing power-related needs. They should also talk about how they used the DevOps tools.
B. Describe a deep learning problem you solved.
As a response to this open-ended question, candidates should describe the project problem. They should explain the different deep learning-based alternatives they explored. Candidates should explain why they chose one alternative. They should explain the implementation process.
C. Explain how you have used labeled and unlabeled data while implementing ML algorithms
This is an open-ended question. Expect the candidates to describe how they have used supervised, unsupervised, and semi-supervised ML learning algorithms for appropriate data sets.
Submit a Project With Zero Risk
If you are looking for expert ML engineers, DevTeam.Space is a great place to start. We have a large community of field expert ML developers and can connect you to the most suitable one for your project.
You are more than welcome to contact us by filling out a DevTeam.Space product specification form to ask about our expert AI engineers. After filling out the form, one of our dedicated account managers will get back to you to answer any questions you might have.