Look for These Key Skills When Hiring a Machine Learning Engineer

Many people picture machine learning (ML) engineers as mythical hybrids of software developers and wizards who effortlessly conjure intelligent insights from massive databases. However, as with most things involving AI and large language models, the reality is far less magical and a whole lot messier.

The truth is, finding a competent ML engineer is less about hunting a tech unicorn and more about identifying specific machine learning skills that can genuinely impact your business outcomes. 

When you know which technical abilities and soft skills really matter, the hiring process becomes much more straightforward.

In this article, you’ll learn the essential skills to look for when selecting an ML engineer so you can confidently identify talent that matches your company’s needs.

Essential Technical Skills to Prioritize When Hiring an ML Engineer 

Just like everything in AI, there's much more to machine learning engineers and their capabilities than meets the eye. 

This section outlines the skill set a machine learning engineer should ideally have. But keep in mind that this is a dynamic, evolving field, and specialists often develop deeper expertise in certain areas. 

In other words, while these in-demand skills should form the foundation of their knowledge of machine learning, it’s rare that ML engineers are experts across all these domains. 

1. Python proficiency: The backbone of ML projects

At the heart of effective machine learning lies a powerful foundation in Python, backed by statistical knowledge and hands-on experience with and expertise in industry frameworks.

For ML engineers, Python isn’t just useful—it’s essential. In fact, Python was the most-used language on GitHub in 2024, surpassing Java largely thanks to machine learning’s explosive growth. Even the popularity of Jupyter Notebooks skyrocketed as ML professionals began to rely more heavily on Python-based data analysis and visualization.

Key Python libraries that every competent ML engineer should know:

• TensorFlow and PyTorch: These libraries allow engineers to build sophisticated neural networks essential for tasks like image and speech recognition, recommendation engines, and natural language processing (NLP).
• Scikit-learn: Ideal for smaller-scale ML projects, this free, open-source Python library is widely used for predictive modeling and statistical analysis because of its user-friendly interface.
• Pandas: This library helps ML engineers efficiently handle, transform, and analyze large datasets needed for clean and structured data inputs.

Python's widespread adoption means collaborating with engineers who truly understand these libraries gives you a significant edge. 

If you’re finding limited expertise locally, it may be worthwhile to expand your search to include the broader international talent pool—perhaps by bringing on a nearshore machine learning engineer. This strategy can connect you with the specialized talent you need to maintain your competitive position in the market.

2. Programming expertise: Beyond Python alone

Python dominates the machine learning landscape and powers most ML projects, but it’s worthwhile to consider other programming languages that can enhance your ML solutions. 

Languages like Java, R, and C++ each bring unique strengths to the table. Java offers advantages when building robust, large-scale systems where application stability is critical. R is particularly valuable for statistical modeling and data analysis, making it popular in research-heavy fields like data science. C++ delivers powerful performance in computationally intensive tasks such as those needed for real-time applications like autonomous driving or gaming systems.

If your team needs an ML engineer who can work effectively across multiple languages, specialized staffing agencies can help you quickly identify versatile candidates who will meet your diverse technical and business requirements.

3. Solid background in statistics and mathematics

Mathematical and statistical foundations are the backbone of successful ML models. Any qualified ML engineer should have a solid grasp of probability, statistics, linear algebra, and calculus. These practical skills aren’t just theoretical requirements—they directly impact an engineer's ability to design, refine, optimize, and deploy accurate models. 

Strong statistical knowledge enables engineers to test hypotheses and interpret complex data patterns. Meanwhile, calculus is crucial for model optimization, particularly for essential processes like gradient descent and efficient model training.

Hiring someone with strong mathematical and statistical fundamentals in their skill set reduces the risk of costly errors and leads to faster model development cycles and more reliable predictions.

4. Hands-on experience with ML frameworks and platforms 

When hiring ML talent, prioritize hands-on, practical experience with ML frameworks like TensorFlow, PyTorch, or Keras. These tools dramatically streamline both the building and deployment process, enabling ML engineers to rapidly create sophisticated and powerful neural networks and implement models efficiently. 

TensorFlow and PyTorch stand out for their flexibility and scalability, allowing engineers to iterate and improve models quickly. Keras offers exceptional user-friendliness that’s perfect for rapid prototyping, which is especially valuable for agile projects or proof-of-concept tasks.

Bringing in candidates who already know these frameworks cuts down on training requirements and narrows the gap between development and deployment, ultimately helping your project reach the market faster.

5. Data management and cloud storage skills

Solid data management skills are essential for machine learning project success. Look for candidates who can effectively handle large datasets and comfortably navigate cloud platforms like AWS, Google Cloud, and Amazon S3. 

ML engineers proficient in these platforms manage data pipelines, implement secure data storage solutions, and retrieve large-scale datasets efficiently, accelerating development workflows.

Cloud storage offers scalable, cost-effective, and reliable data handling capabilities. If you’re finding it challenging to source talent with these specific cloud technology skills, consider partnering with data analyst recruitment agencies that specialize in tech talent familiar with platforms like AWS or Google Cloud.

6. Algorithmic and statistical knowledge

An engineer’s understanding of machine learning algorithms will directly impact your model’s performance and accuracy. Strong candidates should have a firm grasp of key algorithms such as decision trees, regression models, clustering algorithms, and neural networks. They should also be able to clearly explain when and why each one makes sense to use. 

They should be able to comfortably articulate both the strengths and limitations of different algorithms, helping you identify optimal solutions for your specific business challenges.

Solid statistical expertise goes hand in hand with algorithmic knowledge, allowing ML engineers to interpret results correctly, gain deeper insights, and adjust models for better accuracy or efficiency. 

When you bring someone on board who excels in both statistical reasoning and algorithm design, you’re setting up your ML initiatives for consistently reliable outcomes.

7. Cloud platform expertise

Expertise in major cloud platforms—whether it’s AWS, Google Cloud (Vertex AI), or Microsoft Azure—significantly enhances ML capabilities. These platforms offer specialized tools that streamline model development, training, deployment, and monitoring. 

Familiarity with these cloud tools saves time, reduces project complexity, and simplifies the management of large-scale initiatives.

Additionally, engineers experienced with cloud platforms and development tend to excel in remote or offshore arrangements, which expands your hiring options and makes hiring easier. When evaluating remote candidates, your remote hiring process should include asking strategic interview questions designed specifically to allow ML engineers to demonstrate their proficiency in cloud-based deployments.

Soft Skills to Look for in an ML Engineer 

While technical expertise is foundational and essential, it's the soft skills that truly round out a top-performing machine learning engineer. 

These abilities foster effective teamwork, collaboration, creative problem-solving, and adaptability in a field that is evolving at a pace most find difficult to keep up with. 

1. Analytical thinking and problem-solving

ML engineers tackle complex challenges every day, from debugging code to optimizing model performance. 

Strong analytical skills enable them to identify patterns in data, make informed decisions, and pivot quickly when models behave unpredictably or need adjustments to better service business objectives.

2. Clear communication and effective collaboration

Building successful machine learning models requires seamless collaboration with data scientists, product managers, and other stakeholders as well as excellent communication skills.

An engineer who can translate complex technical concepts into simple, actionable insights is incredibly invaluable, especially when teams work remotely or in distributed environments. 

3. Adaptability and continuous learning

With new algorithms, frameworks, and tools emerging constantly, ML engineers must stay on top of industry trends. 

Those who embrace continuous learning and quickly adapt to new technologies are more likely to help ensure that your models remain competitive. 

Skills for Remote ML Teams

When building a remote machine learning team, finding self-motivated engineers who can work independently and who have strong time management skills is key to staying productive without constant supervision. 

Cultural awareness and the ability to collaborate effectively across time zones are also vital for smooth communication and teamwork. 

Targeted remote interview questions will help you assess the presence of these qualities and identify candidates who will thrive in a distributed work environment, keeping your ML projects on track and your team working in sync.

Final Thoughts

Looking to build a strong machine learning team? Finding the right ML engineer can be challenging. While technical proficiency in Python and frameworks like TensorFlow and PyTorch are must-haves, don’t underestimate the importance of soft skills like communication and problem-solving. 

And if you’re hiring for a remote role, skills like independence and cultural awareness can make or break team collaboration.

Now that you know what to look for, it’s time to fine-tune your interview process. But how do you spot and assess these crucial skills during interviews? 

We’ve got you covered. At Near, we know what it takes to find and retain top-tier machine-learning talent from Latin America. Our expert recruiters have compiled a list of the 17 best interview questions to ask to help you identify the perfect ML engineer for your team. 

Ready to make a strategic hire? Explore our guide and begin your search with confidence.