How to Hire Machine Learning Engineers: Skills, Costs and Hiring Guide

Key Takeaways

• A strong machine learning engineer combines software, data, and model skills with experience building systems that work in real settings.

• Companies should define the role before comparing salaries or starting interviews.

• Practical evaluation helps show how candidates solve ML problems and make technical decisions.

• The Flock helps companies access vetted LATAM tech talent with the skills needed for machine learning and AI work.

What Does a Machine Learning Engineer Do?

A machine learning engineer builds systems that use machine learning in real products and workflows.

The role often sits between software engineering and data. ML engineers may prepare data, train models, test performance, connect models to applications, and monitor how they behave after launch.

The exact role depends on the product. Some teams need someone focused on model development. Others need a stronger software background because the main challenge is getting models into production and keeping them reliable.

McKinsey’s latest work on the state of AI reflects how AI is moving deeper into core business functions and workflows. That makes clear role definition more important when companies hire machine learning engineers.

Which Machine Learning Engineer Skills Matter Most?

The right skills depend on the product and technical setup.

Common machine learning engineer skills include:

• Python or another relevant programming language

• Machine learning methods

• Data handling

• Model testing

• APIs and software integration

• Cloud or deployment experience

• Monitoring and debugging

Technical skills are only part of the role. Companies should also look for judgment, curiosity, problem-solving, and clear communication.

ML work often involves trade-offs. A model may perform well in testing but be too slow, too expensive, or too hard to maintain in a live product.

Strong engineers can explain those trade-offs and choose an approach that fits the real need.

What Experience Should Companies Look For?

Past work with real systems can tell companies more than a long list of tools.

A candidate may know several ML frameworks but have little experience taking a model from testing to production.

Useful experience can include:

• Working on real product or business use cases

• Deploying models

• Building data pipelines

• Monitoring performance

• Handling production issues

• Working with software and product teams

The goal is to understand what the person has actually owned.

A candidate who has worked through real model failures, data issues, or system changes may bring more value than someone whose experience is limited to experiments. This is especially important for senior roles, where the engineer may need to make decisions with less guidance.

How Much Does It Cost to Hire a Machine Learning Engineer?

A machine learning engineer's salary can vary a lot.

The main factors are:

• Seniority

• Location

• Technical depth

• Industry experience

• AI specialization

• Production experience

• Hiring model

A junior ML engineer and a senior engineer who can design and run production systems will have very different costs.

PwC’s 2026 AI Jobs Barometer shows that jobs requiring AI skills are growing faster than the wider job market and that those skills carry a higher wage premium. That helps explain why specialized ML talent can cost more than broader software roles.

Companies should compare costs based on the work the person will own, not just the job title.

How Should Companies Evaluate ML Candidates?

The interview process should reflect the work the engineer will do. Coding tests can help, but they should not be the only signal.

A practical scenario can show how someone thinks.

For example:

A model performs well in testing but gives unstable results after launch. How would you investigate the problem?

A strong answer may explore the data, model behavior, monitoring, system setup, and changes in user behavior.

Useful machine learning engineer interview questions include:

• How do you know when a model is ready for production?

• How do you handle poor-quality data?

• What would you monitor after launch?

• How do you choose between two model approaches?

• Tell us about a model that did not work as expected.

The goal is to understand how the candidate solves problems and makes decisions.

What Makes ML Hiring Hard?

Machine learning roles can be hard to define because they often sit across several areas.

A company may need software engineering, data, cloud, modeling, and product skills in one role. Asking for too much can make the search very narrow.

The talent market also remains competitive.

BCG’s 2026 research shows that the global race for AI talent is still strong, even as international movement among highly skilled professionals has slowed. This makes it important to separate must-have skills from skills that can be learned or covered by others on the team.

A clear role gives companies a better chance of finding the right person faster.

Where Should Companies Look for Machine Learning Engineers?

Companies have several options. They can hire locally, search globally, work with contractors, or use a specialized talent partner.

The best option depends on:

• Hiring speed

• Budget

• Time-zone needs

• Location

• Internal recruiting capacity

• Type of role

LATAM can be a strong option for U.S. companies that need skilled AI talent with good working-hour overlap. For teams that need to move fast, starting with an existing vetted talent pool can also reduce sourcing and screening work.

Why Companies Hire ML Talent Through The Flock

At The Flock, companies can access vetted LATAM tech talent across machine learning, software, data, and AI roles.

The validation process covers technical skills, language skills, soft skills, cultural fit, and how professionals work with AI in real workflows. This helps companies start with candidates who already meet key standards.

Talent also works in U.S.-aligned time zones, which makes daily collaboration with product and engineering teams easier.

For companies looking to hire machine learning engineers, this can reduce sourcing work and help them focus on finding the right fit for the product and team.

FAQs About Hiring Machine Learning Engineers

1. What skills should a machine learning engineer have?

Look for strong programming, machine learning, data, deployment, and problem-solving skills, plus experience with real systems.

2. How do you interview a machine learning engineer?

Use technical questions and practical ML scenarios to see how the candidate solves problems and handles real system issues.

3. How much does it cost to hire a machine learning engineer?

Costs depend on seniority, location, specialization, production experience, and hiring model.

4. What is the difference between a machine learning engineer and a data scientist?

ML engineers usually focus more on building and running ML systems, while data scientists may focus more on analysis and modeling.

5. Where can companies find vetted machine learning engineers?

Companies can work with The Flock to access vetted LATAM tech talent based on their ML, product, and team needs.