Machine learning is no longer a niche experiment. It’s a core driver of business growth. Companies across industries are racing to hire machine learning engineers who can turn raw data into smarter products, faster decisions, and new revenue streams.

But hiring isn’t easy.

CompTIA’s 2024 Tech Workforce Report found that 87% of organizations struggle to hire AI developers, including machine learning engineers. Salaries reflect the demand. According to recent Indeed data, ML engineers in the U.S. earn an average of $170,592 per year, with top earners making over $280,000.

Many companies end up stuck. Positions stay open for months, projects stall, and competitors move ahead.

So, how are successful companies responding?

They’re expanding their talent search beyond local markets, tapping into global pools of experienced ML engineers, and building strong teams without burning through budgets.

This guide walks you through how to hire machine learning engineers who drive results efficiently, affordably, and with confidence.

Key Takeaways

• Define your ML needs clearly before starting the hiring process. Know whether you need NLP expertise, computer vision skills, or general ML capabilities to avoid wasting time on mismatched candidates.
  
• Look beyond technical skills when evaluating ML engineers. Communication abilities and project management experience often determine success more than advanced degrees or certifications.
  
• Consider global talent pools to access specialized ML expertise at 30-70% lower costs. Regions like Colombia and the Philippines offer strong technical talent with cultural alignment for US businesses.
  
• Use practical assessments instead of theoretical questions. Give candidates real business problems to solve and evaluate their approach to data cleaning, model selection, and deployment strategies.
  
• Partner with specialized staffing providers like NeoWork to accelerate hiring from months to weeks while accessing pre-vetted ML engineers who can integrate seamlessly with your team.

What Does a Machine Learning Engineer Do?

Machine learning engineers turn data science into real-world impact. While data scientists focus on research and insights, ML engineers build the infrastructure and systems that bring machine learning models to life in production environments.

They’re the ones who make sure models don’t just work in a notebook, but also in the real world, at scale.

Here’s what their work typically involves:

• Translating Business Needs Into Technical Plans: They meet with stakeholders to understand goals, then convert those into clear engineering requirements.
• Building and Maintaining Data Pipelines: They create systems to collect, clean, and prepare data so it’s ready for training and testing.
• Developing and Optimizing Models: They select the right algorithms, train the models, and fine-tune performance to meet business goals.
• Deploying Models Into Production: They write robust, efficient code to serve models at scale, handling real-time requests and large data volumes.
• Monitoring and Improving Performance: They track how models behave in the real world, watching for drift, degradation, or accuracy issues, and updating as needed.
• Collaborating Across Teams: They work with product managers, engineers, and designers to integrate ML into actual features that users interact with.

This role calls for a blend of skills: strong coding ability, deep understanding of ML algorithms, familiarity with tools like TensorFlow or PyTorch, and clear communication. Great ML engineers are as comfortable debugging infrastructure as they are explaining tradeoffs to non-technical teams.

How to Hire Machine Learning Engineers

Hiring great ML talent takes more than posting a job and hoping for the best. You need a process that moves fast, without cutting corners. Move too slowly, and top candidates disappear. Move too fast and you risk bringing on someone who can’t deliver.

This step-by-step approach helps you strike the right balance: thorough evaluation without unnecessary delays.

Step 1: Define Your ML Project Requirements

Start with a clear picture of what success looks like. What business problem should this engineer help solve? Be specific. A goal like “reduce customer churn by 20%” is far more useful than something vague like “build ML models.”

Outline:

• The problem you’re solving
• The type of data you work with
• The ML methods likely to be used (e.g. classification, forecasting, recommendation)
• Where the ML solution needs to integrate (backend, app, CRM, etc.)

Also, consider your team’s current ML maturity. Are you hiring someone to join an established ML function or to build it from scratch? The answer affects everything from the skills you need to the kind of support the role requires.

Be honest about:

• Timelines
• Tools and resources available
• Whether this role is highly independent or part of a collaborative setup

A well-scoped role attracts stronger candidates and sets the foundation for a faster, smoother hiring process.

Step 2: Choose Your Hiring Strategy

Once you’ve defined what you need, the next step is deciding how to find the right person for the job. There are three main hiring paths, each with pros, cons, and ideal use cases.

Start with direct hiring if you want full control over the process. This route gives you complete ownership, from job postings to interviews and final offers. It’s a good fit for companies with established hiring systems and internal ML expertise to evaluate candidates. Please note that it typically takes 2 to 4 months and requires significant hands-on involvement.

For short-term or exploratory projects, freelance platforms can work. They let you move fast, often getting someone started within days. You can scale involvement up or down as needed. But here’s the trade-off: true ML expertise is hard to vet, and freelancers may not be ideal for long-term work or complex production systems.

Then there’s the fastest path, working with specialized staffing partners like NeoWork. These firms pre-vet candidates, run technical screens, and match you with engineers based on your specific project needs. The timeline shortens dramatically—from months to just 2–3 weeks. Plus, they handle contracts, payments, and equipment, freeing you to focus on onboarding and results.

Choose the approach that fits your timeline, budget, and in-house capacity to assess technical talent. You don’t need to use just one. Many teams mix strategies depending on the role.

Step 3: Source Candidates from Multiple Channels

With your hiring strategy in place, the next step is getting your role in front of the right people, and that means casting a wide net without lowering your standards.

Start by targeting the right platforms. Skip generic job boards that flood you with unqualified applicants. Instead, post on ML-specific sites like ML Jobs List, AI Jobs Board, and Data Science Central. Be upfront in your listing: include clear technical requirements, project goals, and salary ranges to filter candidates early.

Then go beyond job posts. Proactive outreach often yields better results. Many of the best ML engineers aren’t browsing job boards. Find them where they work and share, GitHub, Kaggle, Google Scholar. Look for contributors on relevant projects, high performers in competitions, and paper authors in your domain. When reaching out, make it personal. Reference their work and explain what challenge they’d be solving if they joined your team.

Finally, expand your search geographically. Don’t limit your options to local talent. Engineers in Colombia, for example, often bring strong skills, high reliability, and better time zone alignment than offshore teams in Europe or South Asia. Plus, the cultural compatibility helps smooth out communication and collaboration.

The broader and smarter your sourcing approach, the more likely you are to connect with an engineer who not only fits the role, but adds long-term value to your team.

Step 4: Screen Applications Efficiently

Once applications start rolling in, the key is moving fast without sacrificing quality. A structured, repeatable screening process helps you spot high-potential candidates quickly and avoid wasting time on resumes that don’t meet the mark.

Start by using a clear scoring rubric. Weigh the most important traits to your project. A sample breakdown might look like this:

• Relevant ML experience – 30%
• Technical skill match – 25%
• Problem-solving evidence – 20%
• Communication skills – 15%
• Cultural fit indicators – 10%

Apply this consistently across all applications to reduce bias and speed up decision-making.

Then move to a practical assessment. One that respects their time.
Instead of a generic coding test, send a 1–2 hour task inspired by your actual business problem. Ask the candidate to:

• Outline their approach
• Flag potential challenges
• Estimate how long a full solution might take

This not only tests technical skills, but also gives insight into how they think and whether they care about solving your problems.

The goal is to keep momentum. With the right system in place, you can screen confidently and move strong candidates into deeper conversations without unnecessary delays.

Step 5: Conduct Technical Interviews That Reveal True Expertise

By the time a candidate reaches the interview stage, your goal is to go beyond surface-level skills and get a clear picture of how they think, solve problems, and collaborate.

Start with system design discussions based on real scenarios. Skip theoretical puzzles and focus on business-relevant challenges. For example:

"Design a fraud detection system for our payment platform."

Pay attention to how they respond. Do they ask smart questions about data availability? Do they explore different modeling approaches and consider deployment constraints? This shows how they approach ambiguity and complex systems.

Then move into code reviews, using real ML implementations. Share a Jupyter notebook with working, but imperfect, code. Ask them to walk through it and suggest improvements. Look for insights around:

• Optimization
• Handling edge cases
• Writing maintainable, well-documented code

This mirrors what they'll actually do on your team and shows how they handle existing codebases.

Finally, take a deep dive into their past projects. Instead of asking what they know, ask what they did. Explore:

• Their specific role and contributions
• Key technical decisions and why they made them
• Challenges they faced and how they responded
• What they’d do differently today

Great engineers are honest about trade-offs and can clearly explain the why behind their work, not just the what. These conversations reveal far more than any multiple-choice test ever could.

Step 6: Evaluate Practical Skills Through Real Work

Resumes and interviews only go so far. The most reliable way to assess an ML engineer’s abilities is to see how they handle real problems, the kind your team actually faces.

Design a take-home project that mirrors your work. This gives you a window into how they think, code, and make decisions. To keep it fair and useful:

• Scope it for 4–8 hours of focused work
• Use realistic, anonymized data from your domain
• Include clear success criteria, expected outputs, and technical constraints
• Pay candidates for their time. It shows respect and sets the tone for a professional relationship
• Focus your evaluation on how they approach the problem, not just the outcome

Then, schedule a follow-up conversation to discuss their solution. Talk through:

• Why they chose a particular method
• What trade-offs they considered
• How they’d scale or improve the solution with more time
• How they respond to feedback or critique

Often, the way a candidate explains their thought process and handles discussion reveals more than the code itself. You’re hiring someone to solve complex, evolving problems, not just pass a test. This step helps you see how they’ll really perform on the job.

Step 7: Make Competitive Offers Quickly

When you find the right ML engineer, move fast because top candidates are usually juggling multiple offers. Delays can cost you the hire.

Start by knowing the market. According to Indeed data updated in July 2025, the average salary for a machine learning engineer in the U.S. is $170,592 per year, with a range from $103,285 to $281,762, based on over 2,800 job postings.

For nearshore talent in Latin America, salaries typically range from $30,000 to $80,000 USD annually, offering strong skills at more sustainable rates, often with no dip in quality.

Then, structure a compelling package beyond just salary. Include:

• Learning and development budgets
• Conference attendance or speaking opportunities
• Remote-friendly or flexible working arrangements
• Equity or performance bonuses (when applicable)

In a competitive hiring market, speed and clarity win. The longer you wait, the more likely you’ll lose top talent to someone else who didn’t.

Step 8: Onboard for Long-term Success

Hiring the right engineer is just the beginning. The first 90 days will shape how quickly they ramp up and whether they stay engaged for the long haul.

Start strong in the first week. Focus on laying the groundwork:

• Set up system and tool access
• Introduce key data sources and documentation
• Schedule stakeholder and team introductions
• Assign a technical buddy to provide context, answer questions, and ease the transition

In weeks two through four, give them a meaningful but manageable first project. Choose something with clear boundaries and defined success criteria. It should challenge them without overwhelming them, and give them a chance to show how they work.

Then expand their scope gradually over months two and three. Introduce more complex challenges, loop them into cross-team efforts, and start conversations about their role’s future path. Encourage collaboration and visibility from day one.

Stay close with regular check-ins and early feedback. Address blockers early, celebrate small wins, and make sure they feel seen. If they’re working remotely, be extra intentional about integrating them into the team culture.

Strong onboarding builds trust, accelerates productivity, and sets the tone for everything that comes next. Don’t treat it as an afterthought. It’s your foundation for long-term impact.

Types of Machine Learning Roles You Might Need

Not all machine learning engineers do the same kind of work. Some are generalists, but many specialize in different parts of the ML lifecycle. 

Knowing the difference helps you hire the right person for the job, avoid mismatches, and build stronger, more focused teams.

Data Engineers

Data engineers build the foundation for ML work by creating robust pipelines that collect, clean, and store data from various sources. 

They ensure data quality through validation rules, optimize storage for efficient model training, and manage versioning systems. Without solid data engineering, even the best ML models fail to deliver value in production environments.

ML Research Scientists

Research scientists push the boundaries of what's possible with machine learning. They typically have advanced degrees and stay current with academic literature to experiment with novel approaches. 

These specialists excel when you need cutting-edge solutions for complex problems that standard methods can't solve, translating theoretical advances into practical business applications.

MLOps Engineers

The operational specialists who ensure ML systems run smoothly in production environments. They build automated pipelines for model training and deployment, implement monitoring systems to track performance, and manage the continuous integration of model updates. 

Their work bridges the gap between experimental models and reliable, scalable production systems that deliver consistent business value.

Computer Vision Engineers

Specialists in teaching machines to understand and interpret visual information. They develop systems for object detection, facial recognition, quality inspection, and medical imaging analysis. 

These engineers combine deep learning expertise with image processing techniques to solve problems ranging from autonomous vehicle perception to manufacturing defect detection.

NLP Engineers

Experts in processing and generating human language, building everything from chatbots to document analysis systems. They work with transformer models and language embeddings to create sentiment analysis tools, translation systems, and conversational AI. 

As businesses increasingly rely on text data and natural language interfaces, these specialists become essential for customer engagement and automation.

ML Platform Engineers

The architects who create infrastructure enabling other ML practitioners to work efficiently. They build internal tools for experiment tracking, standardize workflows across teams, and manage compute resources for cost optimization. By creating reusable components and collaborative systems, they multiply the productivity of entire ML organizations.

Essential Skills for Machine Learning Engineers

Hiring the right ML engineer means looking past buzzwords and certifications. The most impactful engineers blend deep technical expertise with problem-solving instincts and strong communication. 

Here’s what to look for when evaluating real-world readiness.

1. Technical Foundation

The technical bar for ML engineers continues to rise as the field matures. Python remains the dominant language, and true proficiency goes far beyond basic syntax. 

Look for engineers who write elegant, efficient code that handles edge cases gracefully. They should navigate NumPy arrays and Pandas dataframes as naturally as breathing, optimizing for both performance and readability.

Mathematical understanding separates competent engineers from those who merely apply pre-built tools. Linear algebra isn't just academic theory. It's how neural networks transform data through layers. Statistics guides proper experiment design and prevents misleading conclusions. 

When an engineer can explain why gradient descent works or how regularization prevents overfitting, you know they understand the fundamentals rather than just following tutorials.

Framework expertise varies by use case, but depth matters more than breadth. An engineer who has deployed TensorFlow models to production and debugged performance issues brings more value than someone who's dabbled in every framework. Look for evidence of real implementation challenges overcome, not just completed online courses.

2. The Data Engineering Connection

Machine learning doesn’t work without clean, reliable data. Great ML engineers often overlap with data engineering in key areas:

• Advanced SQL and Pipeline Skills: They should be comfortable writing complex SQL queries, handling messy datasets, and building pipelines that adapt to changing formats, missing data, and edge cases.
• Feature Engineering Expertise: A well-designed feature often makes more impact than the choice of algorithm. Look for engineers who know when to normalize, when to create interaction terms, and when to leave the data untouched. This instinct comes from hands-on experience, not theory.

3. Production Readiness

Moving from Jupyter notebooks to production systems requires an entirely different skillset. Engineers need to understand:

• Containerization and orchestration for reliable deployments
• API design for model serving at scale
• Monitoring systems that catch performance degradation
• A/B testing frameworks for safe rollouts
• Cost optimization for compute-intensive workloads

The gap between prototype and production trips up many ML projects. Engineers who have successfully navigated this transition bring invaluable experience to your team.

4. Soft Skills That Make the Difference

Technical skills get engineers in the door, but soft skills determine their impact. The best ML engineers approach problems like consultants, asking probing questions about business objectives before jumping to technical solutions. They understand that the fanciest model means nothing if it doesn't solve real problems.

Communication abilities multiply an engineer's value exponentially. Can they explain complex concepts to executives without condescension? Do they document their work so others can maintain it? Can they present findings in ways that drive decisions? These skills turn technical work into business value.

Project management becomes critical as ML initiatives grow in scope. Strong engineers balance experimentation with delivery, setting realistic expectations while pushing boundaries. They identify risks early, communicate challenges clearly, and find creative solutions to constraints. They know when to pursue perfection and when "good enough" serves the business better.

Collaboration defines modern ML work. Engineers rarely work in isolation. They partner with data engineers for pipelines, product managers for requirements, and business analysts for success metrics. Those who build bridges between technical and non-technical teams create the most value.

Build vs. Buy: When to Outsource ML Engineering

Deciding whether to build an in-house ML team or work with external experts is a strategic choice. It shapes how fast you move, how much you spend, and how much control you retain.

There’s no one-size-fits-all answer. The right path depends on your business model, resources, and growth stage.

Build In-House When ML Is Core to Your Business

If machine learning is central to your product or competitive edge, build internally. Think of companies like Netflix or Spotify. Their recommendation systems are the product. In these cases, internal teams give you:

• Full control over IP and data
• Tight alignment with product strategy
• Deep institutional knowledge

This approach makes sense if you already have strong ML leadership, budget for top-tier talent, and the infrastructure to support continuous development.

Outsource When You're Exploring or Scaling Fast

If you're still testing how ML fits your business, or need to move fast without long-term hiring cycles, outsourcing is often the smarter play. It lets you:

• Experiment with ML use cases before committing
• Tap into specialized talent for short-term or one-off projects
• Stay lean while validating concepts

Firms like NeoWork help companies build real ML solutions quickly, without needing to hire full-time teams or invest heavily upfront.

Use a Hybrid Model for Balance and Flexibility

Many growing companies find that a hybrid approach works best. Here’s how that might look:

• Keep a small internal team for core systems and long-term strategy
• Bring in external experts for specific tools, domains (e.g. NLP, computer vision), or temporary scaling
• Shift resources based on need, without carrying full-time overhead for every specialization

This gives you control where it matters most, and flexibility where it doesn’t.

Match Your Approach to Your Stage and Strategy

• Startups often begin with outsourcing to validate ML concepts before hiring
• Enterprises might outsource narrowly (e.g. for a vision project) while keeping core models internal
• Mid-size teams may grow internal talent slowly while leaning on partners to move faster.

In the end, it’s not about building everything yourself or outsourcing everything. It’s about accessing the right expertise at the right time, in a way that supports your goals and budget.

Why Hire Machine Learning Engineers from NeoWork

Hiring skilled ML engineers who actually deliver business value and fit your budget is tough. NeoWork makes it easier.

We connect you with pre-vetted ML engineers from Colombia and the Philippines who bring real-world experience and plug in smoothly with your existing team.

Our 3.2% acceptance rate means you’re getting top-tier talent. Engineers who’ve built everything from recommendation engines to production-ready computer vision systems. They don’t just care about model accuracy. They care about outcomes that move your business forward.

You’ll save 50–70% compared to U.S. salaries, without cutting corners on quality.

• Colombian engineers bring strong technical depth and cultural alignment that makes collaboration feel effortless.
• Filipino engineers are especially skilled in customer-facing ML projects and bring exceptional communication to the table.

NeoWork handles everything operational, from contracts and payroll to equipment and ongoing upskilling, so you can stay focused on delivery.

Need just one engineer for a pilot? Or a full team for a production rollout? Our flexible engagement model scales with you.

Let’s talk. NeoWork helps you move fast, stay lean, and build machine learning solutions that actually get results.

Start Building Your ML Team Today

The companies succeeding with AI aren’t waiting around for perfect local candidates or paying inflated salaries. They’re hiring smarter. They’re tapping into global talent pools, using structured hiring processes, and building distributed ML teams that deliver real business results.

Whether you need a computer vision expert for quality control, an NLP engineer to automate customer interactions, or an MLOps specialist to scale your models, the talent is out there. The challenge isn’t availability. It’s knowing where to look, how to evaluate, and how to engage efficiently.

The ML race isn’t slowing down. Every day you wait is a day your competitors move forward. But with the right hiring strategy and the right partner, you can close the gap fast.

NeoWork helps you do exactly that.

• Access pre-vetted ML engineers from Colombia and the Philippines
• Tap into a 3.2% acceptance rate that ensures real-world expertise
• Choose from flexible engagement models that scale with your needs
• Let us handle contracts, payroll, equipment, and training, so you can stay focused on strategy

Our engineers integrate seamlessly with your existing team and bring experience across computer vision, NLP, MLOps, and large language models. Whether you need one specialist or a full team, we’ll match you with professionals who deliver from day one.

Ready to move fast and hire smarter? Schedule a consultation with NeoWork and start building your ML team today.