How to Hire and Work With ML Engineers (2026 Guide)

Machine learning is no longer a futuristic concept; it’s the engine powering today’s most innovative products, from recommendation systems to self driving cars. At the heart of this revolution are ml engineers, the specialized professionals who build, train, and deploy the intelligent systems that are reshaping our world.

The demand for these experts is soaring. Projections show a staggering 40% growth in the need for AI and ML specialists from 2023 to 2027. With job openings outpacing qualified candidates by more than three to one, finding and retaining top talent has become a critical business challenge. This guide breaks down everything you need to know about the skills, roles, and hiring strategies for ml engineers. For a regional perspective, see Mismo’s guide to hiring offshore talent in Latin America.

The Core Toolkit of ML Engineers

To build intelligent systems, ml engineers rely on a powerful stack of tools and foundational knowledge. They are part software engineer, part data scientist, and part statistician, blending practical coding skills with deep theoretical understanding.

Programming Languages and Frameworks

While a talented engineer can learn any language, Python is the undisputed king in machine learning. If you’re curious why, see the reasons Python has become so popular recently. Its simple syntax and massive ecosystem of libraries make it the top choice for an estimated 78% to 87% of data scientists.

• Python: The primary language for building and training models.
• SQL: Essential for pulling and manipulating data from databases.
• R, C++, and Java: Used for specialized statistical tasks or performance critical applications.

Engineers don’t build everything from scratch. They use machine learning frameworks, which are pre built libraries that streamline development. About 82% of data scientists use frameworks like TensorFlow or PyTorch. Popular choices include:

• Scikit learn: A versatile library for traditional ML tasks.
• TensorFlow and PyTorch: The go to frameworks for deep learning.
• Keras and XGBoost: User friendly APIs and powerful gradient boosting libraries, respectively.

Data Infrastructure: Cloud and Big Data

Modern machine learning runs on data, often massive amounts of it. That’s why proficiency with big data tools and cloud platforms is non negotiable.

• Big Data Tools: Technologies like Apache Spark and Hadoop allow engineers to process datasets that are too large for a single machine. Spark, used by nearly 20,000 companies, can be up to 100 times faster than older methods for certain tasks.
• Cloud Platforms: Roughly 90% of data teams use the cloud. Services like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure provide the scalable computing power (like GPUs), storage, and managed ML services needed for serious AI development.

Foundational Math and Statistics

Beneath every algorithm is a foundation of mathematics. A strong grasp of linear algebra, calculus, probability, and statistics is what separates a good ML engineer from a great one. This knowledge allows them to understand why a model works, troubleshoot it when it fails, and even customize algorithms. Even with the rise of complex deep learning, foundational methods like logistic regression are still used by nearly 70% of data professionals, proving that core statistical thinking remains essential.

The Machine Learning Workflow from Start to Finish

Building a machine learning model is an iterative process that moves from messy raw data to a polished, deployed product.

Working with Data

The old saying “garbage in, garbage out” is the first rule of machine learning. The quality of the model is directly tied to the quality of the data.

• Data Preprocessing: This is the critical step of cleaning, formatting, and preparing raw data. It involves handling missing values, standardizing formats, and removing outliers. It’s often the most time consuming part of the job, taking up to 80% of an engineer’s time.
• Feature Engineering: This is the art of creating new input variables (features) from existing data to improve model performance. For example, creating a “time since last purchase” feature from transaction logs. While powerful, it remains a largely manual process, with only about 10% of practitioners using automated tools.
• Data Visualization: Using charts and graphs to explore data and communicate results. Tools like Matplotlib and Seaborn, often used within Jupyter Notebooks (a favorite of 85% of data scientists), help engineers uncover patterns and present their findings to stakeholders.

Building, Optimizing, and Deploying Models

Once the data is ready, the engineering work begins.

• Model Optimization: This involves tuning the model to achieve the best possible performance. It includes selecting the right algorithm and fine tuning its settings, a process called hyperparameter tuning. While automated tools exist, they are only used by about 12% of professionals, meaning many still rely on manual experimentation.
• Model Deployment: This is the “last mile” where a trained model is integrated into a live application, often served via an API. It’s a major hurdle where many projects fail. In fact, some studies suggest only 22% of models built by companies actually make it into production. Building a robust testing culture is key (see the importance of quality assurance).
• MLOps (Machine Learning Operations): To solve the deployment problem, the industry has adopted MLOps. It’s a set of practices that applies DevOps principles to machine learning, creating automated pipelines for continuously training, testing, deploying, and monitoring models in a reliable and scalable way.

Specializations in Machine Learning

The field is vast, and many ml engineers specialize in specific subfields that are driving major technological advancements.

Deep Learning

A subfield of ML that uses multi layered neural networks to solve complex problems. It’s the technology behind breakthroughs in everything from image recognition to natural language. The adoption is widespread, with 82% of data scientists now using deep learning frameworks like TensorFlow and PyTorch.

Natural Language Processing (NLP)

NLP gives computers the ability to understand and generate human language. It powers chatbots, language translation, and virtual assistants like Siri and Alexa. Fueled by models like GPT, the NLP market is projected to triple in value in just a few years.

Computer Vision

This field teaches computers to “see” and interpret visual information from images and videos. In 2015, computer vision models achieved superhuman performance on the ImageNet benchmark. Today, it’s the core technology for self driving cars, facial recognition, and medical image analysis.

Reinforcement Learning (RL)

RL is a paradigm where an AI agent learns by trial and error, receiving rewards or penalties for its actions. It’s famously the technique DeepMind used to train AlphaGo to beat the world’s best Go player. While incredibly powerful for games and robotics, it’s a more advanced technique used by a smaller fraction of practitioners.

The Modern ML Engineer Profile

Beyond technical chops, what makes a successful ML engineer? It’s a combination of practical experience, soft skills, and a clear career focus.

Essential Soft Skills and Qualifications

Technical skills get you in the door, but soft skills determine your impact. Communication, collaboration, and problem solving are paramount. An estimated 91% of companies believe strong soft skills are just as important as technical ones.

In terms of qualifications, a bachelor’s degree in a quantitative field like computer science is the baseline. However, the market is competitive; 94% of ML engineer job listings are for non entry level positions, and many top roles prefer candidates with a Master’s or PhD.

Common Types of ML Engineers

The title “ML Engineer” can mean different things. Common specializations include:

• Applied ML Engineer: A generalist who solves business problems with existing ML models.
• Research Scientist: Focuses on developing novel algorithms and pushing the state of the art.
• MLOps Engineer: Specializes in building the infrastructure for deploying and managing models at scale.
• Domain Specialist: An expert in a specific area, like a Computer Vision Engineer or NLP Engineer.

How to Hire and Retain Top ML Engineers

In a competitive market, having a smart and efficient hiring strategy is key.

The Hiring Process

A typical process involves a resume screen, a take home assignment or coding test, and a series of technical and cultural interviews. A key insight for employers: a smooth process matters. Nearly 70% of engineers report frustration with disorganized interviews, and 57% would decline an offer if there are too many rounds. Furthermore, 60% of engineers prefer a practical take home assignment over a high pressure live coding test.

Compensation and Retention

ML engineers are well compensated. The average base salary in the U.S. is around $133,000, but total compensation at top firms can be much higher. A strong retention strategy is just as important as a competitive salary. This includes offering challenging work, clear career growth paths, and a positive work culture. Without these, you risk losing talent; in a region like Latin America, for example, 78% of engineers planned to look for a new job in the next year, with higher pay being a primary motivator.

The Latin America Advantage for ML Talent

As companies struggle to fill roles domestically, many are discovering the immense pool of talent in Latin America. The region has become a go to destination for building high performing remote engineering teams. If you’re weighing sourcing models, compare onshore, nearshore, and offshore outsourcing.

Why Hire ML Engineers in LatAm?

1. Massive Talent Pool: Latin America has over a million professional developers, with countries like Mexico and Brazil producing hundreds of thousands of new engineering graduates each year.
2. Significant Cost Savings: Companies can save 60% or more on fully loaded talent costs compared to U.S. hires without sacrificing quality. This allows you to build a larger, more impactful team for the same budget.
3. Time Zone Alignment: LatAm engineers work during the same hours as U.S. teams, enabling real time collaboration that’s impossible with traditional offshore locations.
4. Strong Cultural Alignment: Professionals in the region often have high English proficiency and are well versed in U.S. business culture, making for a smooth integration.

The growth is undeniable, with remote hiring of LatAm talent increasing by 161% since 2023. If you want to scale your team quickly and efficiently, this is the place to look. For a real‑world example, see our case study with Revinate on developing a hotel guest platform. Ready to build your world class team? Mismo can connect you with top engineers in LatAm.

Skill Expectations and Best Practices

The technical bar for LatAm ml engineers is the same as anywhere else. They are expected to have a strong command of Python, ML frameworks, cloud platforms, and the underlying mathematical principles. Critically, strong English communication skills are a must for collaborating with international teams, a skill that 45% of LatAm engineers actively prioritize improving. For market context, explore tech talent trends in Latin America.

Hiring successfully in the region involves a few best practices:

• Move Quickly: The market is competitive, so an efficient hiring process is crucial.
• Pay Competitively: Offer a premium salary relative to the local market to attract the best.
• Handle Compliance Correctly: Use an Employer of Record (EOR) to manage local contracts, payroll, and benefits legally. For tax considerations, review our guide to remote employees taxes.
• Integrate Fully: Treat your LatAm engineers as core members of the team to foster a sense of belonging and drive retention. See practical tips for remote team building in Latin America.

Of course, navigating international hiring comes with challenges like legal compliance and logistics. That’s why many companies partner with experts who handle it all for them. A partner like Mismo can manage the entire lifecycle, from sourcing and vetting to payroll and ongoing engagement, making it simple to tap into this incredible talent pool.

Conclusion

ML engineers are the architects of our increasingly intelligent future. Understanding their skills, workflow, and motivations is the first step to building a team that can innovate and compete. For companies looking for a strategic edge, the vibrant, cost effective, and time zone aligned talent pool in Latin America represents an unparalleled opportunity to scale faster and build better products.

Frequently Asked Questions

What does an ML engineer do day to day?

A typical day for ml engineers involves a mix of activities: writing code in Python to clean data or build models, collaborating with data scientists and product managers, experimenting with different algorithms, tuning hyperparameters, and working on deploying models to production environments using cloud services and MLOps tools.

What is the difference between a data scientist and an ML engineer?

There’s overlap, but the focus is different. Data scientists often focus more on analysis, exploration, and deriving business insights from data, building prototypes to prove a hypothesis. ML engineers focus on the engineering side: building robust, scalable, and production ready machine learning systems. They turn the prototypes into real products.

Is Python the only language for ML engineers?

While Python is the most dominant language by a wide margin due to its powerful libraries (TensorFlow, PyTorch, scikit learn), it’s not the only one. ML engineers also frequently use SQL for data retrieval and may use languages like C++ or Java for performance critical parts of a system.

Why is Latin America a good place to hire ml engineers?

Latin America offers a powerful combination of a large, highly skilled talent pool, significant cost savings (often 60%+ compared to the U.S.), and the huge operational advantage of working in the same time zones as North American teams, which allows for seamless, real time collaboration.

How long does it take to hire an ML engineer?

In the U.S., the process can often take several months due to intense competition. However, by using a streamlined process and tapping into a broader talent pool, the timeline can be dramatically shortened. For instance, specialized services like Mismo can help companies hire pre vetted engineers from Latin America in under four weeks.

What are the most important skills for an ML engineer?

The most critical skills are a strong proficiency in Python and ML frameworks, a solid understanding of statistics and mathematical concepts, experience with cloud platforms like AWS or GCP, and practical knowledge of the end to end machine learning lifecycle, from data preprocessing to model deployment (MLOps). Strong communication and problem solving skills are also essential.