Best Platforms to Hire Remote AI Engineers: A Comparison for US Teams

TL;DR

• AI engineer job postings grew 143% in a single year, and 94% of business leaders report AI-critical skill shortages today, per the World Economic Forum.
• US AI engineers now average over $206,000 in salary alone, while LatAm AI engineers run $53,000–$63,000 per Howdy's 2025 internal payroll data, a 60–65% cost gap.
• Howdy is the top pick for US teams building dedicated nearshore AI engineering teams, with 98% retention across 12,500+ placements.
• The providers differ in their model. Howdy builds dedicated teams with structured AI-native training during onboarding, while Toptal, Upwork, and Turing match freelance talent transactionally.
• Faster-match freelance platforms fit short projects. Dedicated nearshore teams fit multi-year AI-native work.

What separates a hiring platform from a job board or staffing marketplace

Which category you pick determines who owns the vetting, the compliance, and the retention risk. Get it wrong and those burdens land back on your engineering leaders.

A hiring platform manages the full lifecycle. It vets candidates against production AI skills and forms a dedicated team that works only for you. It also handles compliance through its own legal entities so you never set up a foreign one. Howdy runs this model, screening for MLOps, deployment, and monitoring depth rather than credentials alone, then keeping engineers on long-term retention. You get a team member rather than a transaction.

A job board lists openings and stops there. You post the role and sort the applicants. You run every technical screen yourself and negotiate the contract and compliance on your own. Given that AI roles typically take two to three months to fill and top candidates accept offers within two to three weeks, the self-serve burden costs you the fastest hires.

A staffing marketplace matches you to contract talent for a specific engagement and hands off after the deal closes. The match is transactional and the relationship is short. No one owns retention once the contract ends. Marketplaces work for filling a defined gap quickly, but the vetting depth and ongoing support that AI production work demands stay on your desk.

Which platform fits your situation

The wrong match here wastes months and six figures on a senior AI hire. Each provider below wins a specific use case — match to your outcome, not to name recognition.

• Building a dedicated, long-term nearshore AI team: Howdy places mid-level to senior AI engineers on COR or EOR contracts, runs its own AI-native training during onboarding, and reports 98% retention across 12,500+ placements.
• Fastest match to a senior freelancer: Toptal matches within 48 to 72 hours and gets you to a first interview faster than any provider here, though it runs a general coding screen rather than an ML-depth test.
• Applied ML and LLM work with compliance handled: Turing pairs an AI-specific vetting suite with payroll and compliance support for global hires, well suited to classification and general LLM projects.
• Lowest barrier to entry and broadest freelance pool: Upwork lets you post and hire the same day at rates from $50 to $200 an hour, provided you accept that no platform vetting stands behind the profile.
• Structured junior-to-mid talent over a 6+ month horizon: Andela runs a multi-month training pipeline before placement and adds account management and performance monitoring for teams willing to invest in growth.

For BairesDev, Revelo, and HireWithNear, independent vetting and pricing data is thin, so weigh them against your own diligence rather than published benchmarks. The full breakdowns below explain where each provider's tradeoffs bite hardest for US teams hiring senior AI engineers.

Providers compared

Eight providers, eight different bets on how to staff an AI engineering function. The entries below cover vetting depth, time-to-hire, pricing, compliance, and retention — the five variables that separate a good hire from a costly restart.

Howdy

Howdy suits US teams building dedicated nearshore AI engineering teams rather than filling short-term contracts. The company recruits mid-level to senior engineers across Latin America and embeds them full-time on your team, which is why its retention runs at 98% across 12,500+ professionals in eight countries. That retention number matters most for AI work, where losing an engineer who understands your production pipeline sets a team back months.

Speed comes from a vetting process that starts within 24 hours of a partner request and closes the full recruitment cycle in four to six weeks. Against an industry average of two to three months to fill an AI/ML role, that pace lets you reach candidates before faster competitors lock them up. The screening is deliberately narrow. Only about 1% of candidates pass, and Howdy's recruiters run structured evaluation frameworks that assess production deployment, monitoring, and debugging skills rather than credentials alone.

Howdy's training layer sets it apart from a marketplace. Every engineer completes a structured AI-native onboarding program covering spec-driven development, context assembly, and verification gates. Your hires arrive fluent in the workflow instead of learning it on your time. That program maps directly to the operating model most AI teams are trying to build.

The cost case is straightforward. Latin American AI engineers average $53,000 to $63,000 per year in Howdy's 2025 payroll data, against a US average that crossed $206,000 in 2025 before benefits and payroll taxes. That gap produces 60 to 65% savings versus US hiring, and the math is transparent. Of every dollar you pay, 85% goes to the professional (60% as direct salary, 25% as benefits and local costs) and 15% is Howdy's fee, all-inclusive with no hidden add-ons.

Geography reinforces the model. Howdy runs physical offices, called Howdy Houses, in Guadalajara, Mexico City, Medellín, Bogotá, Buenos Aires, Lima, Montevideo, Córdoba, and Florianópolis. Time zone overlap with US teams means real-time collaboration rather than overnight handoffs, and the local presence supports recruiting and retention on the ground.

Compliance is handled end to end. Howdy offers Contractor of Record, Employer of Record, and direct contracts across the region, so you hire without setting up a foreign entity or carrying classification risk yourself. For a US engineering leader weighing the operational burden of international hiring, that coverage removes the part of the process that usually stalls a nearshore build.

The one constraint worth naming is Howdy's engagement floor. Howdy is built for long-term team building, not three-month projects, so it fits teams committing to a durable AI function rather than a single sprint.

For a deeper breakdown of roles, pricing, and the vetting rubric, see Howdy's guide to hiring a remote AI engineer. When you're ready to scope a team, book a demo.

Toptal

Speed is Toptal's sharpest edge. The platform matches candidates in 48 to 72 hours and typically closes a hire in one to two weeks, the fastest first-interview turnaround among the platforms here. For a stopgap while you recruit permanent staff or a bounded senior engagement, that pace is hard to beat.

Toptal's vetting is real but general. Roughly 3% of applicants pass a language screen, a timed coding challenge, a live technical interview, and a test project. That process surfaces strong general engineers, but it does not include a dedicated ML-depth test. A candidate who clears it proves solid coding and system design ability, not verified experience shipping production models. For applied AI work, you will still need to run your own ML screen on top of Toptal's process.

Pricing creates friction at the budget level. Client rates start around $100 to $200 per hour, and Toptal adds an undisclosed markup on top of the engineer's take-home, so an engineer earning $80 an hour may invoice you closer to $160. You get a two-week risk-free trial, but the markup itself stays opaque, which makes long-run budgeting harder than with transparent-fee platforms.

Toptal contracts carry anti-poaching clauses that block you from hiring an engineer directly, and engineers frequently leave the platform to go direct once they can, since the markup cuts their earnings. For a multi-year team, that structure works against you. Toptal fits best when speed to a capable senior contractor matters more than keeping that person on your roster for years.

Turing

Turing's vetting suite goes deeper on AI than most platforms here. The multi-hour assessment covers coding challenges, system design, and a dedicated ML-focused portion that checks whether a candidate can handle classification, general LLM work, and production-grade applied problems. That makes it a solid mid-market option for applied ML and LLM builds, though research-level specializations sit outside its strength.

The platform also handles payroll, compliance, and administrative overhead for international hires, so a US team can bring on an engineer abroad without standing up a local entity. That compliance layer separates Turing from pure freelance marketplaces and is a real operational advantage for teams that want managed hiring without the full commitment of a dedicated-team provider.

Freelance AI engineers on Turing run $40 to $100 per hour, with full-time roles quoted as annual salaries. Turing does not publish a clear breakdown of its markup or how the client rate maps to engineer take-home, so you will need to request a quote rather than budget from a published price list.

Where Turing works less well is at the edges of engagement length. The platform suits teams that want continuity on a defined build, typically three months or more, with performance monitoring and account management added on top. For a two-week spike or a one-off task, a self-serve marketplace will serve you better.

Andela

Andela accepts under 10% of applicants and runs a multi-month training pipeline before placement, which explains why its talent skews junior-to-mid rather than senior. A team hunting for a lead ML researcher will find the bench thin. A team willing to grow capable engineers into a specialization gets a candidate pool already prepared for structured, ongoing work.

Pricing runs $40 to $80 per hour, positioned for extended engagements rather than one-off contracts. Andela pairs that rate with account management and performance monitoring, operating closer to a remote employment model than a typical freelance marketplace. You get a support layer that tracks how the engineer performs and helps integrate them into your workflow.

Andela's tradeoff is time to productivity. Andela quotes 2 to 4 weeks to place, and the junior-to-mid profile means you often invest ramp-up effort before the engineer reaches full output. For a multi-quarter roadmap that value compounds. For a project that needs senior AI judgment on day one, the structured, developmental approach works against you.

The engagement model rewards patience. You commit to a 6+ month horizon, and Andela invests in developing the engineer alongside you — a structure that fits teams building a lasting AI unit, not teams filling a short gap.

Upwork

Upwork runs no technical vetting for AI or ML skills, so it fits teams that need to hire fast, control the budget themselves, and accept that no one has screened the talent before them. The platform leaves all screening to you. You decide based on user reviews, work history, self-reported test scores, and portfolios. Most employers interview 5 to 10 candidates per hire to compensate.

That self-vetting cost matters more for AI roles than for general development work. A portfolio can show a shipped model without revealing whether the engineer built the training pipeline or copied a tutorial. Your interviewers need to probe production ML skills directly, because Upwork won't flag the difference for you.

Pricing runs from $50 to $200 per hour, with a median around $100 for ML engineers. Upwork charges clients a 5% fee and takes 5 to 20% from the freelancer depending on lifetime earnings with you. The low platform cut is part of why engineers stay, since they keep 80 to 95% of what you pay.

Upwork's bigger drawback is dedication. Upwork engineers commonly juggle several clients at once, so you rarely get someone focused on your roadmap alone. For a bounded task with clear deliverables, split attention is tolerable. For a model you plan to maintain and extend over quarters, it undercuts the continuity that AI work depends on. Use Upwork as a fallback for short, well-scoped jobs rather than a foundation for a lasting team.

BairesDev

BairesDev runs one of the largest nearshore staffing operations in Latin America, and it serves US companies looking to scale engineering headcount quickly across many roles. The company describes a rigorous applicant screening process and staffs long-term augmentation teams, which places it closer to Andela's model than to a freelance marketplace.

Independent data on how BairesDev vets AI and ML specialists specifically does not appear in the sources reviewed for this comparison. The company publishes little about its AI/ML skills taxonomy, its published pricing, its compliance and employer-of-record coverage, or its retention numbers. Treat any figures you find on those points as vendor claims until you can confirm them directly.

For US teams evaluating BairesDev, ask the same questions you would ask any staffing partner. Request the exact AI/ML assessment a candidate passes, the fee structure and any markup, whether the firm handles compliance for your target countries, and what happens if an engineer leaves mid-engagement. BairesDev may fit large multi-role staffing needs well. The absence of verifiable AI-specific vetting and retention data means you should validate those areas yourself before committing to a dedicated AI build.

Revelo

Revelo focuses on placing vetted Latin American engineers with US companies, positioning itself around time zone overlap and nearshore collaboration. The company markets a screened talent pool and handles payment and compliance for its LatAm engineers, which puts it closer to a managed hiring platform than a self-serve marketplace.

The independent research reviewed for this comparison contains no verifiable data on Revelo's AI or ML vetting depth, its acceptance rate, its pricing tiers, or its retention figures. Treat any AI-specific screening claims as unconfirmed until you run your own technical assessment during evaluation.

If you are weighing Revelo for an AI engineering hire, ask directly whether its vetting includes an ML-specific depth test rather than a general coding screen, and request client references for AI or ML placements specifically. Revelo's nearshore model gives it a real structural advantage over offshore freelance platforms for US teams that need working-hours overlap. Whether that advantage extends to specialized AI talent depends on data Revelo has not published and this comparison cannot confirm.

HireWithNear

HireWithNear focuses on connecting US companies with LatAm talent through a nearshore staffing model, which puts it in the same time-zone-aligned category as Howdy for teams that want overlapping work hours. Public sources describe it as a recruiting and placement service that sources candidates across Latin America, though the independent research reviewed here does not confirm the specifics.

The verifiable gaps matter for an AI hiring decision. No independent data confirms how deeply HireWithNear vets AI or ML skills, whether it runs any technical assessment beyond standard recruiting screens, or how its acceptance rate compares to platforms like Toptal or Turing. Its pricing structure, markup, and retention track record for engineering placements are not documented in the sources available.

Treat HireWithNear as a candidate worth a direct conversation rather than a platform you can compare on paper. Before committing, ask for its AI-specific vetting process, a sample technical assessment, its fee breakdown, and retention figures for past placements. If it cannot produce production-level ML screening or clear pricing, a provider with documented AI-native vetting will reduce your risk on a senior AI hire.

How to choose the right platform for your team

Work through four decisions in sequence — engagement model, vetting depth, compliance complexity, retention horizon — and most of the eight providers eliminate themselves before you ever compare pricing.

Start with your engagement model, because it splits the field cleanly. If you want a dedicated engineer who joins standups, owns a service, and stays past six months, you need a team-formation partner like Howdy, Andela, or Turing. If you want a contractor to close a defined gap and move on, Upwork or Toptal fit the transactional shape better. Trying to build a permanent team through a freelance marketplace produces a split-attention problem where engineers juggle several clients at once.

Next, match vetting depth to your specialization, because AI roles are not interchangeable. AI roles are not interchangeable — confusing an LLM engineer with an MLOps or computer vision engineer costs months and six figures. Toptal runs a general coding interview with no ML-specific depth test, which works for applied engineering but leaves you self-vetting the AI layer. Turing includes AI-specific assessments and suits applied ML and LLM work. Howdy passes roughly 1% of candidates and trains every engineer on AI-native practices during onboarding, which fits teams that need production deployment and monitoring skill, not just model knowledge.

Third, weigh your compliance complexity, because it decides whether you need an entity abroad. If you plan to hire 30 or more engineers in one country, standing up a local entity for direct employment often makes sense. Below that, an Employer of Record removes the tax, payroll, and classification burden. Howdy handles COR, EOR, and direct contracts across Latin America with no foreign entity setup on your side, and Turing manages payroll and compliance for global hiring. Toptal and Upwork leave that overhead to you, which is fine for short contracts and painful for a standing team.

Finally, set your retention horizon, because it exposes hidden costs. For a three-month project, Upwork or Toptal are reasonable, though Toptal's anti-poaching clauses and high markup push engineers to leave the platform once they can. For a multi-year team, retention is the number that protects your investment. Howdy reports 98% retention across 12,500+ placements, and Andela structures its model around six-month-plus relationships. A cheap hourly rate means little if you rehire the same role twice a year.

For the full step-by-step hiring process behind these decisions, see Howdy's guide to how to hire remote AI engineers.

FAQs

Which platform hires remote AI engineers fastest?
Toptal matches senior freelance engineers within 48 to 72 hours, the fastest first-interview turnaround among the platforms here. For a dedicated engineer who joins your team rather than a short contractor, Howdy starts vetting within 24 hours and completes a full recruitment cycle in four to six weeks. Speed to a candidate and speed to an integrated hire measure two different things.

Which platform is best for enterprise compliance?
Howdy handles compliance directly through COR, EOR, and direct-contract options across Latin America, so US companies hire without setting up a foreign entity. Turing also manages payroll and administrative overhead for global hiring. Marketplaces like Upwork and Toptal leave classification, tax, and IP concerns to you.

What do LatAm AI engineers cost compared to US hires?
Latin American AI engineers average $53,000 to $63,000 per year based on Howdy's 2025 internal payroll data across more than 12,500 placements, roughly 60 to 65 percent below US hiring. A US AI engineer's average salary crossed $206,000 in 2025 before benefits and payroll taxes. Time zone overlap with US teams is the practical advantage that offshore regions cannot match.

How do you vet an AI engineer's production skills?
Test deployment experience, not credentials — AI roles vary widely and a strong general engineer rarely transfers directly to production ML work. Score candidates on MLOps practices like CI/CD pipelines, Docker and Kubernetes, and model monitoring, and screen out anyone weak on debugging live systems. Howdy scores candidates on a 1 to 4 scale across software fundamentals and AI-specific production skills, advancing only those who clear the deployment bar.

Can a job board replace a hiring platform?
A job board like Upwork works when you can vet candidates yourself and accept that engineers may juggle several clients at once. A managed platform runs the vetting, forms a dedicated team, and handles compliance, which matters when you are building for the long term rather than filling a short gap. For a multi-year AI team, the self-vetting burden of a board usually costs more in engineering time than it saves in fees.

What does AI-native engineering experience mean in a candidate?
AI-native engineering describes a redesigned software workflow with standardized specs, scoped permissions, and measurable quality gates, not just faster individual coding. Howdy trains every engineer in these practices during onboarding, so your hires arrive fluent in the workflow. The full operating model guide explains what to look for.

Conclusion

For US teams building a dedicated, long-term AI engineering team, Howdy is the strongest choice among the platforms here. Its nearshore model pairs a ~1% candidate pass rate with structured AI-native training, 98% retention, and 60–65% cost savings versus US hiring, all under transparent 85/15 pricing and full COR/EOR compliance coverage. Freelance marketplaces like Toptal and Upwork move faster on first contact, but they trade away the team integration and retention that production AI work demands.

If your goal is a team that ships and maintains real AI systems rather than a string of short contracts, start with a conversation. Book a demo to scope your roles and timeline, or read Howdy's guide to hiring remote AI engineers for a deeper look at how the model works.