October 20268 min read
How Tech Startups Can Compete for AI Talent: 7 Hiring Strategies
Competing for experienced artificial intelligence (AI) professionals presents a significant challenge for technology startups. With established tech companies offering substantial salaries, equity packages, and access to advanced AI research, smaller businesses can struggle to attract candidates with the skills to develop and scale their AI capabilities.
However, compensation is not the only factor influencing where AI professionals choose to work. Opportunities to lead projects, shape technical strategy, and contribute directly to product development can make startups attractive alternatives to larger employers.
Hailey Thompson, a technology talent specialist at Glocomms, has observed this challenge among growing AI companies:
You find if you're working with a mid-size or a startup company, they're asking for people who [...] have been the authors of putting out an AI model. So, it is a little bit challenging.
For startups, the challenge is finding ways to compete for experienced AI professionals without relying solely on the compensation packages offered by major technology companies.
The most effective approach is to develop a hiring strategy that combines competitive compensation, meaningful technical ownership, realistic role requirements, and a clear proposition for candidates.
Here are seven strategies technology startups can use to strengthen their position in the AI talent market.
1. Compete on opportunity, not salary alone
Large technology companies often have the financial resources to offer compensation packages that smaller businesses cannot match.
Charlie Tulio, a consultant at Glocomms, has observed senior AI engineering salaries of approximately $180,000 to $225,000 in the US market, with bonuses and equity increasing total compensation further.
For startups, attempting to compete directly with the highest-paying technology employers may be financially unrealistic.
Instead, businesses should consider what they can offer beyond salary. Hailey highlights the appeal of greater responsibility within smaller organizations:
If you go to this mid-sized or startup company, you have a lot of ownership in how you build out the AI roadmap and how you roll things out.
Within a large technology organization, an AI engineer might contribute to one component of an established platform. At a startup, that same professional could have responsibility for designing the architecture, selecting technologies, and influencing the direction of the product.
This level of involvement can appeal to candidates who want greater autonomy and a direct connection between their work and business outcomes.
What startups should do: Make technical ownership a central part of the hiring proposition. Explain which decisions candidates will influence, what they will be responsible for delivering, and how their contribution will shape the company's AI strategy.
2. Build a competitive compensation package with equity
While career opportunities matter, compensation remains an important consideration for experienced AI professionals. Startups need to understand current salary expectations and structure packages that reflect the responsibilities and experience required.
Charlie has observed a significant gap between the compensation available at some established AI companies and the budgets of smaller businesses.
He recommends considering equity alongside base salary when discussing opportunities with experienced candidates:
You might not necessarily be able to pay those rates [...], but if you offer more on the equity portion and sell the dream of where this company is going, how fast they're looking to grow, that definitely helps.
Equity can provide candidates with a financial interest in the company's future performance. However, its value is uncertain and should be communicated transparently.
Startups should clearly explain:
- Base salary, bonuses, and available benefits
- Equity allocation and the form of ownership offered
- Vesting schedules and any exercise requirements
- Potential dilution and the risks associated with equity
- How compensation may develop as the business grows
A compelling package should balance immediate financial needs with potential longer-term rewards.
What startups should do: Benchmark compensation against relevant competitors, locations, and experience levels before approaching candidates. Where salary flexibility is limited, consider whether equity, benefits, or working arrangements can strengthen the overall offer.
3. Give AI professionals meaningful technical ownership
For many experienced AI engineers, the opportunity to work on technically challenging problems is an important career consideration.
Startups can offer something that may be harder to access within larger organizations: direct responsibility for developing new products and systems.
This is particularly relevant for professionals interested in generative AI, machine learning, and agentic AI.
Rather than joining a large team focused on maintaining established systems, candidates may have opportunities to influence decisions across the entire development process.
These responsibilities could include:
- Selecting models and supporting technologies
- Designing AI application architecture
- Developing and testing new capabilities
- Establishing deployment and monitoring processes
- Working directly with founders and senior leadership
- Contributing to longer-term product strategy
However, startups should distinguish genuine autonomy from simply expecting employees to manage excessive workloads.
Candidates need to understand the resources, technical support, and decision-making authority available to them.
What startups should do: During interviews, demonstrate the technical challenges candidates would own and explain how their decisions would influence the product. Be specific about the level of responsibility rather than relying on broad promises of career progression.
4. Be realistic about the AI experience you require
One of the biggest mistakes startups can make is defining hiring requirements around an ideal candidate profile that may be difficult to attract.
For example, a growing business might seek someone who has developed foundation models at a major AI company, led engineering teams, built cloud infrastructure, and delivered production AI applications.
Finding someone with all these capabilities can be challenging, particularly when competing against employers offering significantly higher compensation.
Startups should instead identify the skills directly connected to their immediate business objectives.
Consider whether the company needs:
- An AI engineer to integrate existing models into a product
- A machine learning engineer to develop and deploy predictive models
- A data engineer to build reliable data pipelines
- An MLOps specialist to support deployment and monitoring
- A senior technical leader to establish an AI strategy and guide delivery
These roles require different expertise and should not automatically be combined into one position.
Hiring managers should also distinguish between capabilities candidates need from their first day and those they could develop after joining.
A software engineer with strong Python, backend development, and cloud experience may be able to transition into certain applied AI roles, especially with relevant project experience.
What startups should do: Prioritize demonstrable technical capabilities over previous employer names or experience with a specific AI platform. This can help broaden the candidate pool without compromising the requirements of the position.
5. Offer career development through emerging AI technologies
AI professionals often want opportunities to develop their technical expertise as technologies evolve.
Startups working on new AI applications can appeal to candidates interested in experimenting with emerging approaches and contributing to early-stage development.
Hailey and Charlie have both observed growing client interest in agentic AI, particularly professionals capable of developing AI agents and integrating them into business workflows.
For startups, these projects can offer candidates exposure to complex technical problems and opportunities to influence how new technologies are implemented.
However, employers should avoid presenting access to emerging technology as a substitute for professional development.
A credible career proposition should explain:
- Which AI technologies the company currently uses
- What technical challenges the team is working to solve
- How employees can develop specialist expertise
- Whether time and resources are available for research and experimentation
- What progression opportunities exist as the organization expands
Startups should also consider how candidates can contribute to technical decisions rather than simply implementing an established roadmap.
What startups should do: Demonstrate how joining the business will help candidates develop relevant technical capabilities and take on greater responsibility over time.
6. Make the Hiring Process Efficient and Technically Relevant
Startups may have fewer financial resources than established technology companies, but they can often offer a more responsive hiring process.
Lengthy interview schedules, unclear technical assessments, and delayed decisions can weaken an employer's position when candidates are considering multiple opportunities.
A focused process can help businesses assess technical expertise while maintaining candidate engagement.
An effective approach could include:
- Initial discussion: Establish technical experience, career motivations, and compensation expectations.
- Technical assessment: Evaluate practical skills using a relevant engineering problem or project discussion.
- Leadership interview: Explore technical ownership, business objectives, and how the candidate would contribute to the team.
- Offer discussion: Communicate compensation, equity, responsibilities, and next steps clearly.
The number of stages should reflect the complexity and seniority of the position.
Technical assessments should focus on skills candidates will use in the role. For example, a startup hiring an AI engineer to integrate LLMs into an application may gain more value from discussing architecture, evaluation, and deployment than from testing advanced model training techniques.
Startups should also establish decision-making responsibilities before beginning interviews to avoid unnecessary delays.
What startups should do: Agree on essential hiring criteria, interview stages, and decision-makers in advance. Communicate timelines clearly and provide timely feedback throughout the process.
7. Build an AI Hiring Strategy That Supports Long-Term Growth
Hiring an experienced AI engineer may address an immediate technical requirement, but startups also need to consider how their teams will develop as products and customer demand expand.
Early hiring decisions can influence future technology choices, infrastructure requirements, and the organization's ability to scale.
For example, a startup initially focused on developing an AI prototype may eventually require additional expertise in cloud infrastructure, data engineering, cybersecurity, and machine learning operations.
Charlie has highlighted the importance of planning for these supporting capabilities:
AI needs to scale, and there's a lot of information that's going into these models. So having those individuals who can do that infrastructure, build out those data pipelines [...] it all has to come together.
This means startups should consider the wider technical capabilities needed to support AI development, rather than concentrating exclusively on model engineering.
A longer-term workforce plan should identify which positions are needed immediately, which capabilities can be developed internally, and when additional specialist expertise may be required.
Contract professionals can also provide flexibility for defined projects, allowing businesses to access technical expertise without immediately expanding their permanent workforce.
What startups should do: Map anticipated AI projects against the technical capabilities needed to deliver and maintain them. Review hiring priorities as the business grows and its infrastructure requirements change.
How Can Startups Attract AI Talent Without Big Tech Budgets?
Technology startups do not necessarily need to match the compensation packages of the largest AI companies to attract experienced professionals.
However, they do need a clear understanding of what candidates value and how their opportunities compare with competing employers.
Competitive salaries, transparent equity arrangements, meaningful technical ownership, and opportunities to work on emerging technologies can all strengthen a startup's hiring proposition.
Equally important are realistic role requirements and an efficient hiring process that respects candidates' time.
For growing businesses, the priority should be identifying professionals whose experience aligns with immediate technical objectives while building the capabilities needed for future development.
Build Your AI Team With Glocomms
At Glocomms, we support technology startups and established organizations seeking specialist professionals across AI, machine learning, software engineering, and data analytics.
Our Software & Analytics specialists work with businesses to understand their technical requirements, identify relevant talent, and provide insight into compensation expectations and candidate availability.
Through permanent, contract, and multi-hire talent solutions, we help organizations develop hiring strategies aligned with their business priorities.
Looking to attract experienced AI professionals?
Contact Glocomms to discuss your hiring requirements and explore how we can support your AI talent strategy.