How to hire AI engineers: A guide for recruiters

In the AI boom, everyone’s keen to make the most of new technologies. And for some companies, that means hiring for brand new roles. 

Hiring any new position is easier said than done. You need clear criteria to evaluate their skills and adaptability alongside any relevant experience, and with new AI features constantly releasing, it’s hard to know if you’re keeping up with the curve. 

Here’s how to know what the role really requires, how to choose between candidates, where to source them, and what to budget. 

What does the AI engineer role entail?

AI engineers are responsible for using AI and machine learning (ML) to build new models, applications, AI systems, or processes. These engineers find ways for AI to boost efficiency and automate processes or solve problems, with goals usually tied to business outcomes.

AI engineers might:

• Prepare data: An AI model is only as effective as the data it’s trained on. Engineers clean, label, and structure raw data into a usable training set, handling missing values, duplicates, inconsistent formats, and class imbalances that would otherwise skew the model.
• Deploy new models: If an engineer is working on a new update or feature that integrates AI, they’ll need to deploy that new model. That means wrapping it in an API or service, managing versioning so they can roll back a bad release, setting up monitoring for drift and latency, and making sure the model holds up under real traffic rather than a test set.
• Scale a model: Once a process is working, it’s time to expand usage. Engineers optimize inference costs, handle higher request volumes, and adapt the model to new use cases or departments, often retraining as the data distribution shifts away from what it was originally built on.

How AI engineers differ from adjacent roles

Although there might be overlap in some responsibilities, AI engineers are not:

• AI researchers: Researchers are focused on developing algorithms and ML techniques that AI engineers then use in practice. Researchers focus more on theory and developing AI capabilities. 
• Data scientists: Data scientists are responsible for turning raw data into insights that businesses can use for better decision-making. Whether using statistics or ML models, a data scientist’s job is to identify patterns in the data and use it to find solutions to problems. 
• ML-focused developers: Developers take data and turn it into an algorithm, allowing computers to learn new information. They use the foundations of AI researchers and the outputs of data scientists to develop an AI model that performs a particular task. 

Core AI engineer skills 

Here are the core competencies AI engineers need to have:

• Programming: Engineers need to know programming languages like Python, TensorFlow, PyTorch, or Java to implement models. 
• Basic data science: AI engineers need to work with large amounts of data to build and implement AI models. Experience working with data pipelines is a must-have when hiring an AI engineer. 
• Algorithms: Although AI engineers don’t necessarily build AI models, they need enough grounding to diagnose why a model is underperforming and reason about tradeoffs like accuracy against latency and cost.
• Problem-solving: AI engineers need to troubleshoot while they’re developing and deploying a new model. Creative problem solving and strong collaboration skills help engineers work efficiently and effectively. 

Choosing the right hiring model for AI engineers

Before writing up a job description, decide what you need to gain from hiring an AI engineer. How dependent your business is on AI, how much data you have available, how big the team is, and what stage the project is at all impact who you’ll want to hire. Once you know all of this information, you’re ready to start searching. 

Core hiring models and when each model fits best

You have several models to choose from when hiring an AI engineer, but each comes with tradeoffs. 

Freelance

Working with a freelancer gives you flexibility in working arrangements. You’ll be paying hourly or by project, not at a fixed salary. Plus, freelancers aren’t full employees, so you’ll save on benefits and payroll costs. This hiring model is best for prototypes or short experiments, where the work is bounded and you don’t need to commit to a permanent hire.

The downsides are that, since freelancers aren't full employees, they often don’t have the same depth of context about your company and how it operates. This lack of familiarity might mean rockier working relationships or extra time spent getting freelancers up to speed. 

In-house

Hiring an AI engineer to be a full-time employee means you have access to their expertise all the time. You can work on multiple AI projects at once and enjoy predictable costs for every project. While freelancers might rack up more hours than you’d expected, an in-house engineer would already be part of your budget. They’re a great option if you have long-term programs that need maintenance or continued rollout. 

In-house AI engineers need to be busy, though, to be a worthwhile expense. If you don’t have a full pipeline of AI projects or continuous work for an engineer to do, it might be more expensive to bring them onto the team. 

Tech partners

A tech partner is an agency that supplies a team (engineers, project manager, designers, etc.) under contract. Working with tech partners gives you access to an AI engineer’s expertise without the risk of paying someone to sit idle. Paying a partner also means that you’re paying for a team’s expertise, not just one person’s. The cost is generally fixed, and there’s little overhead for training or onboarding an in-house employee. Tech partners are a great option for customer-facing systems that are complex and require high upfront investments. You’re paying to work with a team that has experience building a similar product, so there’s less risk of getting it wrong.

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Tech partners often come at a higher price tag, though, and have less flexibility in working terms. You’ll want to have a very clear idea of the project at hand before getting involved with a partner. 

Where to source AI engineers

Finding the best engineers requires more than just posting the job in the typical places like LinkedIn or Indeed. The strongest candidates are likely already employed, so you’ll need to reach them directly. Here’s where to look for them.

Technical communities and talent platforms

AI engineers often share information in online communities like GitHub, Kaggle, Discord, Hugging Face, and Stack Overflow. And, if you find talent on GitHub or Kaggle, you’re also finding some proof of work.  

Events, global channels, and agencies

Academic conferences, like (NeurIPS or ICML), are a great place to find research-adjacent hires who might not normally respond to cold outreach. And widening your remote talent markets like Eastern Europe or Africa surfaces skilled workers who live on regional job boards instead of US-centric ones. Finally, you can work with a specialized AI recruitment agency. Its services will come at a fee, but its network is wider than one you can pull together quickly.

Juicebox

Juicebox is an AI-first recruiting platform that enables recruiters to search for candidates in plain language queries, vetting over 800 million global profiles—including those for specialized engineers. Juicebox short-lists viable candidates with the right experience and skill set in minutes.

How to vet AI candidates

Vetting should test three things: whether the candidate has built and shipped what they claim, how they reason through a problem, and whether they can work across a team. Here’s what to look for.

Portfolio and proof of work

While a strong resume and advanced degree are essential qualifications, you also want proof that the candidate has done the work. Check Kaggle or GitHub projects, review publications, ask about deployed projects, and examine prototypes to see what the candidate has accomplished. 

Interview process

Behavioral and situational interview questions give AI engineers a chance to explain their thinking and walk you through their problem-solving approaches. Ask questions about ML frameworks, deployment, scaling, and debugging. 

Don’t use worst-case scenarios or puzzles to try to find the best candidate. Ask practical, job-shaped questions. 

Practical assessments

Give candidates a small test or take-home assignment to assess their skills. Asking them to debug a program or explain how to scale a model gives you an idea of how they’d actually perform on your team.  

Cost to hire AI engineers

The cost of hiring AI engineers differs widely across experience levels, geographic region, hiring model, and seniority. 

Typical compensations ranges

In the U.S., the median annual pay for an AI engineer is $144,000. The total range, of about $116,000–$182,000, is based on a standard level. According to Glassdoor, advancing to a Senior Manager of Machine Learning comes with an earning potential of $209,000–$334,000 per year.

*Note that the field is constantly evolving and these numbers might become outdated quickly.

Ways to control hiring costs

AI engineers can be expensive, and if budget is a key factor for your organization, you’ll want to keep hiring costs down. Here are a few ways to lower the bill.

• Hire globally: Hiring outside of the U.S. exposes you to talent in regions with lower costs of living and, thus, lower salaries. 
• Hire emerging talent: Less experienced engineers might be more of a risk, but they likely come with a lower price tag. If your project isn’t particularly complex or risky and a candidate has the core skills you need, you may want to take a chance on emerging talent.
• Work with an agency: The hourly rate of working with an agency can be higher than a salary, but total cost is the comparison that matters. You skip the recruiting spend and the vacancy, pay only for the project's duration, and carry no benefits or severance. That beats a permanent hire when the work is bounded and you're unsure you'll need the role in a year.

Move faster on hiring with Juicebox

With such high demand for AI talent, the job market is competitive —and it moves fast. If you aren’t constantly scouring job boards or trying to find new candidates, you might never find the talent you need.

Stay ahead of the curve by keeping your pipeline full with Juicebox. With Juicebox, recruiters and hiring teams have access to over 800 million profiles from more than 30 sources. No matter who you’re hiring or where, Juicebox’s outbound talent sourcing can deliver up to 3 times more replies. 

Book a demo to boost your hiring process today.