ALL ROLES / AI AND ML ENGINEERS
Hire a machine learning engineer who ships models, not just prototypes
Retrieval and LLM features, recommendation, classification and forecasting in production. One dedicated AI and ML engineer from Latin America who builds, deploys and monitors your models with your team, during your hours.
TRUSTED BY TEAMS WHOSE CLOUDTASK HIRES HAVE STAYED 5 TO 8 YEARS
How it works
How do I hire a machine learning engineer?
You do one thing: interview. We handle everything else.
Post the role
5 minutes. The role, the motion, the tools, the comp band. That is all we need to start.
Review your matches
3 to 5 matches in 48 hours.
Interview and hire
We coordinate interviews. On Managed Staffing, we also handle payroll and compliance across LATAM.
WHY TEAMS TRUST CLOUDTASK
What you get with every hire
One engineer, your models
A dedicated person who learns your data, your product and your constraints, instead of a freelancer who hands over a notebook and leaves.
Same hours as your team
Latin America and the Caribbean overlap the US working day, so model issues and product decisions get worked through with your engineers in real time.
Checked on your problem
LLM applications, classic machine learning or both, on the frameworks and cloud you run. We screen each candidate against your brief.
Replacement guarantee
24 months on Managed Staffing, 6 months on Direct Hire. The guarantee covers the case where the fit turns out to be wrong.
WHY CLOUDTASK
Staffing with numbers behind it
10,000+
hires placed
since 2016
85%
still in seat
past 90 days
24 months
replacement guarantee
on Managed Staffing
12
countries
across Latin America and the Caribbean
PRICING MODEL
Managed Staffing.
Managed Staffing is open to every role we place. We find the person, handle payments and compliance, and stay involved after the hire with check-ins, visibility, and escalation when something is off. You get the person. We keep the machine running. For roles that create demand, outbound SDRs, BDRs, demand generation and full-cycle AEs, Managed Staffing runs on a six-month minimum term.
Pay
One all-in monthly rate
Per person. That is the number, and there is nothing added to it.
In the monthly rate
- Sourcing from the CloudTask staffing and recruiting company
- Screening and vetting
- Matching coordination
- Worker payments
- Compliance and employment administration
- Onboarding support at day 0, 7, 30 and 60
- Ongoing check-ins with the hire and with you
- Time and activity tracking, on by default
- Performance escalation
- 24-month replacement guarantee. Replacements not capped.
- Health benefits available on request
- Equipment benefits available on request
Stays with you
- The work itself
- Priorities and direction
- Who you hire
$299 to start your search. One time, not a subscription. It applies to your buyout or toward your first placement.
Start your searchPRICING MODEL
Direct Hire.
Direct Hire is open to every role we place. We find the person, vet them, and hand them over. You employ them, you manage them, you own the relationship from day one. Roles that create demand usually land here, because live call feedback and daily coaching work best coming straight from your sales leader, and there is no minimum term.
Pay
20% to 30%
Of first-year base salary. One time, quoted per role, paid when they accept.
In the fee
- Sourcing from the CloudTask staffing and recruiting company
- Screening and vetting
- Shortlist of matched candidates
- Interview coordination
- Offer support
- 6-month replacement guarantee. One replacement, same job description.
Yours when they accept
- Employment and payroll
- Compliance and employment administration
- Ongoing management
- Time and activity tracking
- Health benefits and equipment benefits
$299 to start your search. One time, not a subscription. It applies to your buyout or toward your first placement.
Start your searchHow CloudTask compares
Run the numbers. We did.
The same role, three ways to fill it.
Swipe to compare
| US Direct Hire | Staffing Agency | ||
|---|---|---|---|
| Time to First Interview | 48 hours | 3 to 6 weeks | 2 to 4 weeks |
| Payroll & Compliance | Included on Managed Staffing | You manage | Included in rate |
| Vetting Process | Human + AI verified | You screen | Recruiter screened |
| Candidate spam | Screened out before you see a profile | Common | Low |
| Tool Verification | Verified per candidate | Self-reported | Rarely verified |
| Free Replacements | 24 months on Managed Staffing, 6 months on Direct Hire | None | Variable |
Testimonials
Trusted by teams that hire with us
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LATAM has been a gold mine for talent. Our CloudTask hires understand our company culture and mission, and they connect with our customers in a way that drives high NPS and CX scores.
David Barrett CEO, Expensify -
At first we were an SF-based team only. Then I met Amir Reiter back in 2018. After visiting Medellín, I knew LATAM would be key to our growth and success.
Tim Zheng CEO, Apollo.io -
After meeting my CX and CS team in Medellín, we immediately loved the culture and the work ethic. As a European-HQ company, we found LATAM to be perfect to service our American clients.
Carl Carell CRO and Co-founder, GetAccept
SKILLS
Top capabilities to look for in a machine learning engineer
The hard part of machine learning is rarely the model. It is the data, the evaluation and keeping it working after launch.
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Production Python
Clean, tested, reviewed Python, not only notebooks. Machine learning code lives inside your product and has to meet the same bar.
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Core machine learning
Choosing, training and tuning models with frameworks such as scikit-learn, PyTorch or TensorFlow, and knowing when a simple model is enough.
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LLM applications
Retrieval augmented generation, prompt design, tool use and fine tuning where it pays off, built on the model providers you choose.
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Evaluation
Test sets, metrics and evaluation suites that tell you whether a change made the model better, before users find out.
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Data pipelines for models
Preparing features and training data reliably, and working with data engineers so the model sees the same data in training and in production.
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Deployment and MLOps
Packaging, serving and versioning models, with experiment tracking and repeatable training runs on your cloud.
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Monitoring after launch
Watching quality, drift, latency and cost in production, and retraining or rolling back when the numbers move.
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Safety and privacy
Handling personal data correctly, guarding LLM features against misuse, and documenting what the model can and cannot do.
HIRING GUIDE
How to hire a machine learning engineer
Hiring a machine learning engineer goes well when you know whether you need a model in production or an answer from your data. This guide covers what the role does, how it differs from a data scientist, when not to hire one, and what to check in the interview.
What does a machine learning engineer do?
A machine learning engineer builds models into products. That means preparing the data, training or adapting a model, evaluating it, deploying it, and keeping it working as the data and the product change.
Today the role often includes AI engineering: building features on top of large language models with retrieval, prompts and evaluation, alongside classic machine learning such as recommendation, classification and forecasting.
Machine learning engineer or data scientist?
A data scientist answers questions and builds models to inform decisions. A machine learning engineer builds models that run inside your product, reliably, at scale.
If the output you need is an analysis or a forecast for the business, look at a data scientist. If it is a feature your users touch, hire a machine learning engineer.
When should you not hire a machine learning engineer?
When you do not yet have a clear use case or the data to support it. The engineer will spend months looking for a problem, and a short discovery project or an analyst is a cheaper way to find one.
It is also the wrong hire when an off the shelf AI tool already solves the problem. Buying a product your team can configure is often faster than building and maintaining your own.
AI engineer or ML engineer?
The titles overlap. AI engineer usually means building on large language models: retrieval, agents, prompts and evaluation. ML engineer usually means training and serving your own models on your own data.
Write the problem in the brief rather than the title. The search is built around the work, and many candidates do both.
What should I check in the interview?
Give a small, real task: a sample of your data and a feature to build, or an LLM feature with a handful of examples of good and bad answers. Look at how the candidate sets up evaluation before improving anything.
Then ask about a model they shipped and what broke after launch. A good answer covers monitoring, data drift and how they found out, not only the model architecture.
Red flags in machine learning engineer candidates
Improving before measuring. In the task, a candidate who starts tuning the model or rewriting prompts before setting up any evaluation cannot tell you whether a change made things better. Strong candidates build the evaluation first.
Nothing broke after launch. Ask about a model they shipped and what went wrong in production. A candidate who only talks about the architecture, with nothing on monitoring, drift or how they found out, has probably not kept a model running.
Notebooks as the deliverable. If their work ends in a notebook that someone else turned into production code, they are not yet writing the tested, reviewed Python that has to live inside your product.
Courses instead of deployments. A candidate who shows certificates but cannot say what they deployed, who used it and how they kept it working is describing learning, not production experience.
The complex model by default. A candidate who reaches for a large model or a custom build without asking whether a simple model or an off the shelf tool would do will spend months on what the problem did not need.
Can someone learn machine learning in 3 months?
Someone can learn the basics and build simple models in that time. Shipping models that hold up in production takes much longer: software engineering, data handling, evaluation and operations.
That is why the interview should test production experience, not course certificates. Ask what they deployed, who used it and how they kept it working.
How much does it cost to hire a machine learning engineer?
It depends on seniority, the stack, the language requirement and the country the person works from, and any single number describes one scenario and calls it a price.
What is worth understanding is the shape. On Managed Staffing it is one all-in monthly rate per person with payments, compliance and replacement cover inside it. On Direct Hire it is a one-time fee and the person goes on your payroll. We quote your number before you interview anyone.
Why hire a machine learning engineer in Latin America?
Overlap. Machine learning work depends on constant back and forth with product, data and backend engineers, and an engineer who works your hours joins that conversation live.
The second reason is communication. Explaining what a model can and cannot do is part of the job, and your engineer works with your team in English every day. We screen that live on a call.
How does CloudTask hire for this role?
You send the problem, the stack and the hours you need covered. We source against that brief and come back with 3 to 5 matched profiles within 48 hours, each one screened on a live call and checked against the frameworks and tools you named.
You interview and choose. On Managed Staffing we handle payments, compliance and onboarding and stay involved with check-ins. On Direct Hire the person joins your payroll from day one. Both carry a replacement guarantee: 24 months on Managed Staffing, 6 months on Direct Hire.
FAQ
The fine print, in plain terms.
What is the difference between an AI engineer and an ML engineer?
Can I learn ML in 3 months?
Do I need a data scientist or a machine learning engineer?
How fast can a machine learning engineer start?
What is the $299 for?
Why is there no number on the monthly rate?
Who is the legal employer?
How does tracking work, and can I turn it off?
What happens if the person does not work out?
Is there a platform or subscription fee?
Ready to hire?
3 to 5 matches in 48 hours. Start with a quick role brief to get your shortlist.