Building a talented and flexible workforce can be the difference between success and failure, especially for AI leaders looking to stand out.
AI hiring managers, HR leaders, and startup founders know that assembling teams for AI data labeling and large language model (LLM) projects is a unique challenge.
You need specialists who can handle sophisticated tasks and a staffing model that scales with your needs.
We built this AI Staffing Hub to get our favorite AI content in one spot.
This post brings together HireArt’s top resources and expert insights on sourcing, hiring, and managing AI contract teams all in one place. Consider it your centralized library for smarter, scalable AI staffing operations.
We created this hub to share key tips from each resource and how they contribute to building a high-performing AI workforce.
Before diving into hiring, it's crucial to ensure your organization is ready to embrace AI initiatives.
Change can be daunting, especially when it comes to new technologies that are misunderstood. Getting leadership buy-in is often the first step to success.
HireArt’s guide How to Push for Sensible AI Adoption at Your Company offers a practical game plan for championing AI projects internally. It outlines five down-to-earth strategies to help you introduce AI in a way that managers and teams will support.
Key insights include:
By following these steps, you’ll create a supportive environment for your incoming AI team. In short, this resource helps you lay the groundwork so that when you bring on new data labelers or ML engineers, the company is ready to integrate and champion their work.
After all, building a smart AI workforce starts with a company culture that “gets” AI.
Now that you’ve convinced your team that AI can solve some serious organizational issues, it’s time to find and evaluate the best data labelers.
While there is a ton of chatter about AI taking jobs, here is one truth: machine learning simply could not exist without human power behind the scenes.
High-quality training data is the fuel for machine learning, and skilled data labelers ensure that models learn correctly.
Hiring for these roles can be tricky, especially with the breakneck speed of the AI race. Luckily, HireArt’s resources provide a blueprint for vetting these candidates effectively.
Our AI Data Labeler Assessment Checklist is a handy one-pager that outlines a three-step vetting process for data annotation hires.
By following this structured approach, you can ensure that every data labeler is prepared from day one. Don’t get bogged down in time- and labor-intensive onboarding and training. The key is to screen for competence before they’re on the job. With fast-moving AI projects, there’s little time to train or replace underperformers.
For a deeper dive, the HireArt blog How to Assess and Recruit Amazing AI Data Labelers expands on those principles with practical screening tips.
Hiring contract data labelers is a nuanced process. With the right strategy, you’ll consistently land top-notch talent. A few highlights from this guide:
Use a Phased Assessment
Don’t overwhelm candidates with hours-long tests upfront.
Instead, break your evaluation into stages (for example, a quick initial screening task, followed by a more detailed assignment for those who pass).
This “slow reveal” of complexity gives you progressive insight into candidates’ capabilities without scaring off good talent or wasting anyone’s time.
Simulate Real Tasks
Tailor your skills tests to mimic the actual data annotation work they’ll be doing. If the role involves labeling images for autonomous vehicles, maybe the assessment includes a small image-labeling project.
The goal is to see how they handle the kind of data and instructions they’ll face on the job. This not only helps you spot the right hires, but candidates appreciate understanding what the job really entails.
Focus on Key Strengths
Great data labelers often share certain soft skills that aren’t obvious from a resume alone. The article notes three in particular to screen for:
These traits can be gleaned through work sample reviews and behavioral interview questions.
Iterate and Improve
Finally, treat your hiring process itself as an evolving product. Collect feedback on your assessments. Identify the best methods for distinguishing the top performers. Note inefficiencies where bad hires slipped through.
By maintaining an ongoing feedback loop and tweaking your vetting process, you continuously improve your hit rate in finding amazing contractors.
Hiring is not “set it and forget it,” especially in AI, where tasks change fast. Treat it like an ongoing optimization.
Using this checklist and guide together will drastically improve your ability to identify contract workers who are a cut above.
When your data annotators are skilled and reliable, it elevates your entire AI operation. Suddenly, models train faster and better, project timelines stay on track, and you spend less time scrambling to replace people. In other words, investing effort upfront in assessment pays off in a smarter, more scalable labeling team.
Pro tip: HireArt’s platform can even help administer tailored assessments and provide vetted candidates, making this process easier!
As you recruit AI talent, one strategic question looms: What kind of employment arrangement is best for these workers? Traditionally, many companies default to using 1099 independent contractors for short-term AI tasks.
But as your AI projects grow in scope and sophistication, that approach might crack under pressure.
We suggest an increasingly popular alternative: “task-based W-2” contract employees. This model brings your contractors on as W-2 employees (often through an Employer of Record service like HireArt) for the duration of the project, rather than as self-employed 1099 freelancers.
Why go the W-2 route for contract hires? In our resource, Contract W2 Employees vs. Independent Contractors for Data Labeling, we present a side-by-side comparison of these worker types in the context of generative AI data labeling.
When AI tasks were simpler and purely repetitive, retaining independent contractors via crowd-sourcing made sense. However, as LLMs and AI become more complex, the balance has shifted to accommodate the need for quality, retention, security, and flexibility.
Training & Quality
With W-2 employees, you can provide direct training and ongoing feedback without legal worries. Complex AI projects often demand detailed guidelines and evolving techniques – something you cannot fully do with a freelancer (labor laws restrict training 1099 contractors).
W-2 team members can be continuously upskilled, resulting in higher-quality output and consistency.
Engagement & Retention
AI companies now require specialists with deep domain expertise (for example, linguists to fine-tune an NLP model, or medical experts for healthcare AI). These folks are hard to find, so once you have them, you want to keep them engaged.
W-2 employment, even on a contract basis, tends to yield better retention and commitment than gig work.
Contractors hired through HireArt, for instance, often stick around longer and feel part of a team, whereas a 1099 freelancer might drop your project the moment a higher-paying gig appears.
Data Security
Data privacy is huge in AI. W-2 contract employees usually work on your systems, with company-provided devices and signed NDAs, making it easier to protect sensitive data. An independent contractor juggling multiple clients on their own laptop presents more exposure risk.
With W-2s, you have more control over the work environment, thus better security and compliance.
Performance Management
Want to require a daily stand-up or set specific working hours?
With freelancers, you legally can’t. In a freelance, 1099 engagement, workers determine how and when work is done. That can hinder real-time collaboration or rapid pivots in a project.
A W-2 employee, on the other hand, can operate within your defined schedule, participate in team meetings, and be subject to performance reviews.
Studies show that engaged employees who get feedback do better work, and W-2 arrangements enable that feedback loop in a way 1099 cannot.
Flexibility Without the Headaches
Perhaps the best part, as Why More AI Companies Are Choosing Task-Based W‑2 Employees explains, is that a W-2 contract model gives you the flexibility of gig work plus the reliability of an employed team.
You still get to scale your staff up or down for short-term needs, but you avoid the pitfalls that come with managing a swarm of freelancers.
Say goodbye to unpaid invoices or ambiguous legal status. Your W-2 contractors are official employees for the duration of your project, meaning taxes are handled and labor laws are satisfied. This drastically lowers the risk of misclassification audits and compliance issues, which are increasingly common in states like California.
In summary, if you want to scale your AI team intelligently, consider the W-2 contract approach for at least your core workers. It offers a sweet spot between full-time hires and gig freelancers.
You get committed, well-trained contributors who integrate with your processes, and you maintain the agility to scale the team as projects demand. HireArt specializes in this model – we handle the payroll, compliance, and HR support for your contract W-2 staff, so you can focus on the projects. The result is a contingent AI workforce that’s nimble and high-performing.
Competitive compensation is the key to attracting and retaining great talent.
But what is the “right” pay for an AI data labeler, an AI trainer, or a machine learning contractor? This is where HireArt’s original research comes in handy.
That’s why we created an annual AI compensation report to share all of our findings over the past few years. This report distills findings from a wide-ranging survey on AI contract worker pay.
For any HR leader building an AI team, these insights are instrumental in shaping salary strategies that are fair and effective.
Here are a few key revelations:
Not All AI Roles Are Paid Equal
The survey analyzed pay rates for a variety of AI-related positions (150+ data points across 20 specializations and 15 countries!). Roles that require multi-faceted skill sets and leadership command the highest rates.
On the other hand, more execution-focused roles earn less depending on the region and experience. This makes sense: project management mixes technical and managerial expertise, which is rarer, whereas general data annotation can be more routine.
The takeaway: budget more for roles that combine skills or carry heavy responsibility, and expect to pay premiums for those.
Specialists Earn More
Within the data labeling/training domain, specialization greatly affects pay.
For instance, a Subject Matter Expert (SME) AI Trainer with, say, a strong background in computer science or biotech can earn dramatically more than a generalist labeler.
Hourly vs. Task-Based Pay Models
A fascinating insight for those managing labeling workflow: the best compensation model can depend on the task type.
For complex, thought-intensive labeling work (like writing nuanced prompts or reviewing AI outputs for bias), paying by the hour is usually more effective. It incentivizes quality and allows the worker to take the necessary time to do the job right.
Conversely, for high-volume, repetitive tasks (think tagging thousands of images for objects), a per-task pay model might drive efficiency without sacrificing accuracy.
Read much more about task-based pay models and the rise W-2 task-based pay in AI projects in our ebook.
Onshore vs. Offshore Mix
Traditionally, the bulk of data labeling was offshored via crowd-work platforms, where workers might earn just a few dollars per task in lower-cost countries. The landscape is shifting.
Many companies now blend in-house or onshore contract teams with offshore labor to get the best of both worlds.
They bring on a skilled team of domestic W-2 contractors (through a partner like HireArt) for mission-critical or highly specialized work. From there, it’s not uncommon for AI companies to supplement with a larger pool of offshore annotators for volume.
The compensation survey reinforces this trend, showing how pay rates vary globally and how companies are willing to pay a premium for in-house control and quality on certain tasks.
Overall, this compensation resource arms you with data to answer “what should we pay our AI contractors?” With these annual benchmarks, you can set salaries or hourly rates that are competitive enough to attract talent and keep your budget sustainable.
It’s about balancing cost and quality—and knowing which levers to pull.
HireArt’s expertise is in acting as a trusted staffing partner; by surveying the market, we help you stay informed and strategic about compensation, so your offers win the talent you need.
We don’t talk enough about how important it is to give your employees and contractors a good experience. Hiring great people is only half the battle.
To truly scale your AI operations, you must prioritize inspiring high performance and retaining that talent.
Contract workers, just like full-time employees, deliver superior results when they feel motivated, valued, and part of a team.
So, how do you maintain high morale and low turnover in a contract AI workforce, especially when they are remote and distributed?
HireArt’s resource, How to Keep Your AI Workforce Engaged and Happy, offers a wealth of common-sense strategies to build an engaged, high-performing AI team.
Here’s a quick rundown of practices you can implement:
Frequent Feedback & Communication
AI projects evolve quickly, and labelers often work on nuanced tasks where clarity is key. Set up real-time feedback loops.
Growth and Advancement Opportunities
Even if your contractors are short-term, providing a sense of progression boosts engagement. Identify star performers and give them a path to grow.
People are happier when they feel recognized for their work. You might even host a brief “recognition ceremony” to celebrate achievements and announce any promotions or added roles.
This shows that great work leads to advancement, which incentivizes everyone to shine.
Team Recognition and Culture
Create a culture where good work is appreciated publicly.
Maybe institute a “Labeler of the Month” shout-out in your team newsletter or virtual meetings. Peer recognition is powerful, too. Encourage team members to give each other kudos for solving a tough problem or hitting quality targets.
These positive reinforcements make contractors feel like they’re part of a team, not just transient gig workers. When people feel valued, they remain enthusiastic and committed, which is exactly what you want for long-term projects.
Clear Communication and Inclusion
Make sure your AI workforce feels in the loop. Kick off each project with a solid onboarding session to clarify goals, timelines, and how each person’s contribution fits into the bigger picture.
Follow up with weekly updates or Q&A sessions hosted by project managers to address any changes or to solicit feedback.
This kind of transparency and accessibility builds trust. An informed team member is an empowered team member – they’ll work more effectively and feel ownership of the outcome when they understand the mission.
Right Tools & Work Environment
Equip your AI team with tools that foster collaboration and ease. Use project management platforms to track progress and secure cloud storage for sharing guidelines or datasets.
Ensure everyone has access to training materials and documentation from day one.
Many top companies create an internal knowledge base or how-to videos for their labeling tasks. This helps new hires ramp up fast and sends a message that you care about setting them up for success.
With these engagement tactics, you can transform a group of contract workers into a cohesive, motivated AI all-star team. When labelers and other AI staff feel supported, they produce higher-quality work. They are also more likely to continue working with you on future projects.
This stability and continuity are huge for reliable scalability. With that, you can take on bigger AI initiatives, secure in the knowledge that your team will stick around and maintain excellence.
HireArt often assists clients with these workforce management best practices, providing not just the people but also guidance on how to keep them happy (we’ve seen what works across many AI teams).
Finally, a happy note: engaged teams tend to refer other great talent. So by keeping your current AI workforce satisfied, you might inadvertently build a pipeline of referrals when you need to scale up – your existing contractors become ambassadors who bring friends/colleagues on board. It’s a virtuous cycle of talent growth driven by positive work culture.
As this Ultimate AI Staffing Hub shows, there’s a lot that goes into assembling and managing an effective AI team.
The good news is you don’t have to do it alone. Just like you, we’re still learning about artificial intelligence talent, how to harness it, and how to create a happy and productive working relationship with contractors.
HireArt aims to serve as your trusted partner in AI workforce strategy. We’ve spent years helping top AI companies (large and small) build contract teams that deliver results.
Our platform and services are designed to take the burden off your shoulders: we source and vet specialized AI talent, serve as the Employer of Record for hassle-free W-2 employment, and provide full HR support to your contract workers.
That means you get top-tier people working on your data labeling, model training, and AI R&D tasks, without the usual administrative headaches. The resources in this hub are a glimpse into our expertise, and we only expect to grow it from here.
We know what makes an AI project succeed, and we’re eager to help you implement these best practices in your own organization.
Whether you’re kickstarting a new ML initiative and need an entire team of labelers or scaling up an existing program with niche experts, HireArt can tailor a staffing solution for your specific needs.
With HireArt as your partner, you can scale your AI contract team confidently. And you can do it without sacrificing quality, compliance, and flexibility.
Ready to elevate your AI staffing game?
Explore the resources linked above, and feel free to reach out to HireArt for a demo or consultation. Together, we’ll turn these insights into an AI team that drives your company’s innovation forward. Here’s to your next great AI hire!
