Data engineering · ML engineers

Hire ML Engineers from Canada, Latin America, and the US

Hire a machine learning engineer who takes your models from prototype to production, and keeps them reliable at scale. We find the right person, or build your entire data team, in Canada, Latin America, or the US, on a contract, permanent, or fractional basis.

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Machine learning engineers, screened by machine learning engineers

DevEngine is a Vancouver-based IT recruitment and staffing firm. We recruit machine learning engineers from Canada, Latin America, and the US for contract, permanent, or fractional roles. As more companies build AI into their products, machine learning engineers are the people who get models into production and keep them running, in industries from healthcare to financial services. A senior ML engineer screens every candidate before you see one. You manage the day-to-day work. We handle contracts, HR, and admin.

The role

An ML engineer puts models into production

A machine learning engineer builds, deploys, and maintains the models that power product features and business processes. They turn a data scientist's prototype into a service that runs reliably, at the right cost and speed, and they monitor it after launch so it keeps performing as the data changes.

Typical outputs include production model services and APIs, training and deployment pipelines, feature pipelines, model monitoring and retraining processes, and integrations of large language models into existing products.

The role is often confused with two others, and organizations define all three differently. In general:

ML engineer

What they work on: Getting models and AI features into production, and keeping them reliable
Core question: Will this model run reliably, at scale, inside our product?
Typical outputs: Model services, deployment pipelines, monitoring
Works most with: Software engineers, data scientists, and platform teams
Often brought in when: A model or AI feature has to ship and stay healthy

Data scientist

What they work on: Predictions, experiments, and models
Core question: What's likely to happen next, and did this change work?
Typical outputs: Demand and revenue forecasts, risk and churn models, A/B test readouts for stakeholders
Works most with: Product, finance, and marketing leads, plus analysts and ML engineers
Often brought in when: A decision needs a forecast or a tested result, not another report

Data engineer

What they work on: The pipelines and platforms that move and store data
Core question: Is the right data arriving, clean and on time, where people need it?
Typical outputs: Data pipelines, warehouses, data models
Works most with: Data scientists, analysts, and platform teams
Often brought in when: Data is scattered, late, or unreliable at the source

	ML engineer	Data scientist	Data engineer
What they work on	Getting models and AI features into production, and keeping them reliable	Predictions, experiments, and models	The pipelines and platforms that move and store data
Core question	Will this model run reliably, at scale, inside our product?	What's likely to happen next, and did this change work?	Is the right data arriving, clean and on time, where people need it?
Typical outputs	Model services, deployment pipelines, monitoring	Demand and revenue forecasts, risk and churn models, A/B test readouts for stakeholders	Data pipelines, warehouses, data models
Works most with	Software engineers, data scientists, and platform teams	Product, finance, and marketing leads, plus analysts and ML engineers	Data scientists, analysts, and platform teams
Often brought in when	A model or AI feature has to ship and stay healthy	A decision needs a forecast or a tested result, not another report	Data is scattered, late, or unreliable at the source

If the work is exploring the data and building the first model, see Hire Data Scientists. If the data feeding the models is the problem, see Hire Data Engineers.

When to hire

When you need an ML engineer

The need usually shows up as one of these situations:

AI investment is stuck at the prototype stage.
Data scientists built models that work in testing, but without engineers who can ship and run them, the business keeps paying for experiments that never reach customers.
AI is on the roadmap, not in production.
Leadership has committed to recommendation, forecasting, or LLM-based features, and the team has never run machine learning in production, putting launch dates and customer commitments at risk.
Models quietly lose accuracy, and revenue follows.
As customer and market data shift, predictions drift, and without monitoring no one notices until pricing, fraud, or demand decisions start costing the business real money.
AI costs are growing faster than AI value.
Inference runs slow or expensive, the cloud bill rises faster than usage, and finance is starting to question whether the models are worth what they cost to run.

Need a different role?

If you need someone to explore the data and decide what to model, hire a data scientist. If the data itself is late or unreliable, hire a data engineer. We recruit for both roles.

Our screen

What our senior machine learning engineers look for

Most candidates have trained a model in Python. Fewer have deployed one as a live service, kept it running under real traffic at a cost the business accepted, and caught it when accuracy slipped as real-world data changed. A senior machine learning engineer screens every candidate and asks for specifics: what shipped, how it performed, what broke, and what they changed.

Models that shipped
We ask for a model they took to production and what it did for the business. A strong ML engineer can describe the system around the model, not only the model.
Monitoring and retraining
We ask how they caught a model degrading and what they did about it.
Cost and speed trade-offs
We ask about a time they made a model faster or cheaper to run, and what they gave up to do it.
Fluency with your constraints
An ML engineer who has worked with your cloud, your data volume, or regulated data reasons differently from one who hasn't. We weight that context, not only certifications.

Candidates who have only built models in notebooks, can't explain how they'd monitor one in production, or treat accuracy as the only metric are screened out. Your interviews focus on fit, not filtering.

Where we source

Hire from Canada, Latin America, or the US

One search can cover all three talent pools. Brief the role once, and compare candidates from each market side by side before you decide.

Canada

Where candidates are based: Across Canada
Working hours: Canadian time zones, Pacific to Atlantic
Cost: Canadian market rates. See the Canada salary guide below.
Fits when: The role needs knowledge of the North American market, regular in-person time in Canada, or a Canadian base

Latin America

Where candidates are based: Across Latin America, including Argentina, Brazil, Colombia, Mexico, and Costa Rica
Working hours: Within 2 hours of US Eastern time, year-round. Up to 4 hours ahead of Pacific time.
Cost: Typically 35–40% below comparable North American cost. See the Latin America salary guide below.
Fits when: You want a wider candidate pool or more seniority for the budget, or the ML engineer will work with a nearshore team

United States

Where candidates are based: Across the United States
Working hours: US time zones, Pacific to Eastern
Cost: US market rates.
Fits when: The role needs knowledge of the US market, regular in-person time at a US site, or a US base

	Canada	Latin America	United States
Where candidates are based	Across Canada	Across Latin America, including Argentina, Brazil, Colombia, Mexico, and Costa Rica	Across the United States
Working hours	Canadian time zones, Pacific to Atlantic	Within 2 hours of US Eastern time, year-round. Up to 4 hours ahead of Pacific time.	US time zones, Pacific to Eastern
Cost	Canadian market rates. See the Canada salary guide below.	Typically 35–40% below comparable North American cost. See the Latin America salary guide below.	US market rates.
Fits when	The role needs knowledge of the North American market, regular in-person time in Canada, or a Canadian base	You want a wider candidate pool or more seniority for the budget, or the ML engineer will work with a nearshore team	The role needs knowledge of the US market, regular in-person time at a US site, or a US base

Not sure which market fits? Tell us about the role, and we'll scope it in each market and show you candidates and cost side by side.

Technology salaries in Canada and Latin America

Benchmark salaries by role and seniority before you set a budget.

Download the Canada Salary Guide Download the Latin America Salary Guide

Engagement models

Contract, permanent, or fractional

Contract

Who employs the ML engineer: DevEngine
Time commitment: Full-time, for a defined period
Fits when: Shipping a specific model or AI feature, or a production rollout with a defined end
Placement protection: Two-week guarantee

Permanent

Who employs the ML engineer: You
Time commitment: Full-time, ongoing
Fits when: Machine learning is part of your product and needs a standing owner
Placement protection: 180-day prorated guarantee from the start date: full refund in the first 30 days, then partial refunds on a sliding scale

Fractional

Who employs the ML engineer: DevEngine
Time commitment: Part-time, hourly or monthly
Fits when: A model review, an MLOps setup, or a cost and performance audit, without a full-time seat
Placement protection: Tailored to the scope of work

	Contract	Permanent	Fractional
Who employs the ML engineer	DevEngine	You	DevEngine
Time commitment	Full-time, for a defined period	Full-time, ongoing	Part-time, hourly or monthly
Fits when	Shipping a specific model or AI feature, or a production rollout with a defined end	Machine learning is part of your product and needs a standing owner	A model review, an MLOps setup, or a cost and performance audit, without a full-time seat
Placement protection	Two-week guarantee	180-day prorated guarantee from the start date: full refund in the first 30 days, then partial refunds on a sliding scale	Tailored to the scope of work

Each model works a little differently depending on whether you hire in Canada, Latin America, or the US. Compare the options by market to find the engagement that fits your team.

Explore services by market

How it works

How an ML engineer search runs

You brief us on the scope and goals of the project and your current machine learning and cloud stack.
We source for your role in Canada, Latin America, or the US, not from a bench.
A senior ML engineer screens each candidate. Someone who does the same work, not a recruiter matching keywords.
You choose who to interview and who to hire.
You direct the work. On contract and fractional engagements, DevEngine holds the contract and handles payroll and compliance. On a permanent hire, the ML engineer joins your payroll.
If the placement isn't working, we replace the candidate: within two weeks on contract, or under the 180-day prorated guarantee on a permanent hire.

FAQ

ML engineer hiring questions

How much does it cost to hire an ML engineer?

Cost depends on four things: seniority, specialization (for example, computer vision, recommendation systems, or LLM applications), the market you hire from, and the engagement model. Hiring in Latin America typically costs 35–40% less than comparable North American hires. For salary benchmarks across technology roles, download the Canada Salary Guide or the Latin America Salary Guide. DevEngine quotes against your actual brief on the first call.

Which skills and certifications do ML engineers bring?

Most work in Python with frameworks such as PyTorch, TensorFlow, or scikit-learn, and in the cloud, container, and pipeline tools your teams already use. Common certifications include the Google Cloud Professional Machine Learning Engineer, the AWS Certified Machine Learning Engineer – Associate, and the Microsoft Certified: Azure AI Engineer Associate. Skills and certifications show familiarity, but not whether someone can keep a model healthy in production. That's what the senior ML engineer screen tests.

Can an ML engineer build features with large language models?

Yes. Many ML engineers now integrate LLMs into products: connecting them to company data, evaluating output quality, and controlling cost and latency. Tell us which models and data you plan to use, and we'll match candidates with that experience.

Can I hire a fractional ML engineer?

Yes. A fractional ML engineer works part-time, on an hourly or monthly cadence, inside your team and tools. Companies often use one for a model review, setting up deployment and monitoring, or a cost and performance audit. DevEngine places fractional ML engineers from Canada, Latin America, and the US.

Should I hire an ML engineer from Canada, Latin America, or the US?

Companies often choose Canada or the US when the role needs local market knowledge, regular in-person time, or a base in that country. Latin America offers a wider pool and more seniority for the budget, with working hours within 2 hours of US Eastern. DevEngine can run the search across all three markets so you can compare candidates.

Can an ML engineer work with a DevEngine Engineering POD?

Yes. An ML engineer can sit inside an Engineering POD or work alongside one. You choose the POD's composition and direct the work. The ML engineer owns the models in production, and the engineers build the product around them on your sprint cadence, in your tools.

What's your interest?

Choose the path that aligns with how you want to build and scale your team.

Tell us what the model has to do.

We'll tell you which market and model fit, and how long the search should take.

Not ready to talk yet? Compare technology salaries in the Canada Salary Guide or the Latin America Salary Guide.