September 20265 min read
AI Engineering Salaries in the US: 2026 Compensation Benchmarks
What should businesses expect to pay for AI engineering talent in the US in 2026, and what is shaping demand for these skills?
To provide a current view of the market, we spoke with Hailey Thompson and Charlie Tulio, both consultants at Glocomms, about the AI roles they are seeing in demand, the skills employers are prioritising, and the compensation required to attract experienced professionals.
Drawing on their work across the US technology talent market, we have compiled salary and total compensation benchmarks for roles including Senior AI Engineer, Principal Generative AI Engineer, and Senior Director of Agentic AI in 2026. Their insights also point to a wider shift in hiring, with agentic AI, data infrastructure, and practical engineering expertise influencing the profiles businesses are searching for.
What is driving demand for AI talent in 2026?
Generative AI has dominated technology conversations in recent years, but Hailey and Charlie are now seeing attention shift towards agentic AI and autonomous workflows.
For businesses, this means moving beyond AI systems that respond to individual prompts towards applications capable of completing tasks and supporting more complex workflows. That shift is naturally influencing the technical expertise employers require.
As Hailey explains:
First, it was generative AI, and now it’s working with agentic workflows and AI agents. When you want senior leaders within the AI space, you really do want someone who is still willing to be hands-on, especially when working with agentic AI.
Demand is therefore not limited to professionals with knowledge of AI models. Businesses need hires who can apply that knowledge within working technology environments, bringing together software engineering, data and infrastructure capabilities.
This is creating opportunities across AI engineering, machine learning engineering, data science and software engineering, while also increasing demand for senior professionals who can combine technical expertise with responsibility for AI strategy.
Which AI skills are employers looking for?
The technical requirements behind AI positions are becoming increasingly specialised.
Demand is steadily growing for professional’s experience across generative AI, agentic AI systems, large language models (LLMs), machine learning, software engineering, and data infrastructure.
Charlie has also seen a distinction emerging between professionals who can work with existing models and those with deeper experience developing AI systems:
Some of the most in-demand skillsets we’re seeing are around building AI agents and large language models, rather than simply taking an existing model and fine-tuning it. That deeper technical capability is increasingly sought after.
For employers, this makes skills-based hiring more relevant. In other words, a job title may provide an initial indication of someone's experience, but it does not necessarily show whether they have the practical expertise required for a specific AI project.
It also creates opportunities to consider professionals from adjacent areas. Software engineers with strong Python experience, for example, may be able to move into full-stack AI engineering or agentic AI positions if they can demonstrate relevant project experience. Data scientists with generative AI and agentic systems experience are another talent pool businesses can consider.
AI engineering salary benchmarks for 2026
Growing demand for these capabilities is reflecting in compensation. Based on market insights shared by Hailey and Charlie, Glocomms is seeing the following salary and total compensation ranges:
| Role or talent profile | Base salary | Total compensation | Market context |
| Senior AI Engineer | $180,000 to $225,000 | $275,000 to $300,000 | Senior AI engineering talent, with equity and bonuses included in total compensation |
| Principal Generative AI Engineer | $180,000 to $220,000 | Not specified | Similar range to Senior AI Engineering |
| Senior Director, Agentic AI / Autonomous Agentic AI | $250,000 to $312,000 | Not specified | Philadelphia, typically around 10 to 15 years' experience |
| Senior AI talent | $300,000+ | Not specified | Senior positions in higher-paying US technology hubs such as the Bay Area/ New York |
| Data Engineer, frontier AI company | Around $350,000 | Around $750,000 | Individual Bay Area example rather than a general market benchmark |
Source: These figures reflect market insights shared by Glocomms consultants Hailey Thompson and Charlie Tulio. Compensation varies according to experience, location, responsibilities, organisation, bonuses, and equity. Individual examples should not be interpreted as guaranteed market rates.
For senior AI engineers specifically, Charlie is seeing base compensation comfortably above the $180,000 mark:
A lot of the senior AI professionals I’m speaking with are getting paid between $180,000 and $225,000. With equity and bonuses, that can go up to around $275,000 to $300,000 in total compensation.
Principal generative AI engineers are operating in a similar market, with Hailey citing base salaries of approximately $180,000 to $220,000.
At a leadership level, the figures rise further.
Location continues to influence AI compensation
Despite greater flexibility in where technology teams operate, location continues to affect AI salary expectations.
The Bay Area and New York remain particularly competitive markets for experienced AI professionals. As Hailey's observations indicate, senior talent in these locations can command higher base salaries than comparable positions in Philadelphia.
At the furthest end of the market, compensation can be considerably higher.
Charlie referenced an individual Data Engineer in the Bay Area whose package was approximately $350,000 in base salary and $750,000 in total compensation, with experience connected to frontier AI organisations.
This is an individual market example rather than a representative Data Engineer salary benchmark, but it illustrates the competition businesses can face when targeting professionals from companies at the forefront of AI development.
For startups and mid-sized businesses, matching compensation at this level may not be realistic. The wider opportunity, including equity, technical ownership and influence over an organisation's AI strategy, can therefore become an important part of the overall proposition.
AI infrastructure is becoming part of the talent challenge
Something else Hailey & Charlie have noted happening across the markets is that AI hiring does not stop with models. As businesses move from experimentation to deploying AI in production, they also need the infrastructure capable of supporting those systems.
Charlie explains:
AI needs to scale, and there’s a lot of information going into these models. You need people who can build the infrastructure and data pipelines that support machine learning and data science teams. It all has to come together.
This means businesses investing in AI may also need machine learning engineers, data engineers, cloud and infrastructure specialists, and software engineers.
It also raises an important workforce planning question. Before adding specialist AI talent, organisations need to understand whether their existing technology environment can support the systems those professionals are being hired to build.
Define the AI role before starting the search
Another challenge is more fundamental: businesses do not always have a clear definition of the AI professional they need.
AI job titles remain relatively fluid, and responsibilities can overlap between AI engineers, machine learning engineers, data scientists, and software engineers.
Charlie has seen the consequences of that ambiguity directly:
I’ve had clients tell me they want an AI engineer, but after writing down what they’re looking for, they end up wanting a data scientist. With AI being so new, there’s still a lot of ambiguity around what companies actually need
Clarifying the requirement before beginning a search can save time and make compensation benchmarking more accurate.
Businesses should establish the following:
- The problem the person will address
- What they will build or own
- Which technical capabilities are required
- How the position fits alongside existing engineering, data, and infrastructure teams
That definition can then inform the job title, seniority, compensation, and target talent pool.
Assessing AI candidates requires greater scrutiny
The growth of generative AI is also changing how businesses assess candidates.
It is difficult to determine whether AI expertise presented on a resume reflects practical experience. AI tools can make it easier to produce sophisticated application materials, which puts greater emphasis on technical screening.
Charlie described situations where candidates appeared highly relevant on paper but struggled to discuss the experience presented on their resume when questioned in greater depth.
For employers, this makes practical assessment particularly important. Rather than relying primarily on job titles or lists of technologies, hiring teams can explore what candidates have built, the technical decisions they made, and how they approached specific engineering problems.
This can also help identify professionals moving into AI from adjacent areas. A software engineer who has built functioning AI agents through professional work or independent projects may have relevant capabilities even without an established AI engineering title.
AI adoption is becoming a retention issue
Businesses must also consider how their AI strategy affects existing technology teams, as it can impact employee retention.
Hailey and Charlie are speaking with professionals who want greater exposure to AI because they see these capabilities becoming increasingly relevant to their careers. Organizations that are slower in providing such opportunities could face a retention challenge, particularly among software engineers, data scientists, and other technical professionals who do not want to fall behind their peers.
For employers, that makes internal skills development an important part of AI workforce planning.
Providing existing employees with opportunities to work on AI projects can strengthen internal capability while giving technical professionals access to the technologies they want to develop. For some positions, developing adjacent talent internally may also provide an alternative to competing for a limited pool of established AI specialists.
How can businesses compete for senior AI talent?
Salary remains a major consideration, particularly when candidates are comparing opportunities with large technology companies and frontier AI organisations.
But compensation is not the only factor employers can influence.
Hailey and Charlie both identify equity, ownership of technical decisions, and influence over AI strategy as areas that can help smaller businesses present a compelling opportunity.
An experienced AI professional joining a smaller organisation may have the opportunity to shape architecture, build a capability at an earlier stage, or take greater ownership of the AI roadmap than they would within a much larger technology company.
Businesses should therefore consider the complete proposition. Compensation needs to reflect the market, but candidates also need clarity around what they will build, the technical problems they will address, the resources available, and the level of influence they will have.
Planning your AI hiring strategy for 2026
The US AI talent market in 2026 extends well beyond a single role or skillset.
Agentic AI is influencing demand for hands-on engineering expertise, while data and infrastructure capabilities are becoming increasingly relevant as businesses move AI systems into production. At the same time, employers are considering adjacent talent pools, developing existing employees, and strengthening technical assessment.
Compensation reflects the competition for these capabilities. Glocomms is seeing senior AI engineer base salaries of $180,000 to $225,000, with total compensation reaching $275,000 to $300,000, while senior leadership positions in agentic AI can command base salaries above $250,000.
For businesses, however, an effective AI hiring strategy starts before compensation is discussed.
Defining what the organisation needs the person to accomplish, identifying the underlying technical skills, and understanding the available talent market can help businesses establish an appropriate role and compensation package.
Whether you are searching for generative AI engineers, agentic AI leaders, machine learning talent, data engineers, or scientists, Glocomms can help you define the skills you need, benchmark your compensation, and connect with specialist AI talent.
Speak to Glocomms about your AI hiring requirements today.