Introduction
The rapid growth of artificial intelligence has given rise to specialized tech roles that command top salaries. Two of the hottest positions are AI Infrastructure Engineers and Machine Learning (ML) Engineers. But which role pays more? In this deep dive, we’ll compare compensation trends for AI infrastructure vs ML engineers in 2024–2025. We’ll break down average salaries by experience level and location, from entry-level packages to senior roles at big tech firms. You’ll also learn how geography (think Silicon Valley vs the national average) and employer type (startup vs FAANG) impact pay. By the end, you’ll have a clear picture of which role might put more in your pocket – and why. (SEO keywords: AI Infrastructure Engineer salary, Machine Learning Engineer salary, tech salary 2025, ML vs AI engineer pay, big tech vs startup salaries)
Role Overview: AI Infrastructure Engineer vs Machine Learning Engineer
Machine Learning Engineers are developers who design, build, and deploy ML models – enabling systems to learn from data. They typically have strong coding skills (e.g. Python, C++), know frameworks like TensorFlow/PyTorch, and understand algorithms and math. ML engineers work across industries (finance, healthcare, retail) to integrate predictive models into real-world application. AI Infrastructure Engineers, on the other hand, focus on the systems and platforms that make large-scale AI possible. They build and maintain the robust pipelines, cloud services, and compute infrastructure needed to train and deploy AI models efficiently. In practice, an AI infrastructure specialist might set up distributed training environments, optimize model serving platforms, or manage GPUs/TPUs clusters for big AI project. Both roles are in high demand – the U.S. Bureau of Labor Statistics projects 26% job growth in AI-related fields this decade, far outpacing the 4% average for all job. Companies are investing heavily in AI, driving up demand (and pay) for experts who can either create powerful models or ensure those models run at scale.
Refonte Learning, a leader in tech education, notes that these roles have become some of the fastest-growing jobs global. In fact, AI engineers and ML specialists are seeing significant salary jumps as AI, cloud computing, and big data dominate tech in 2025. While ML engineers have been around longer as a defined career, the “AI infrastructure” title is newer – emerging from the need to productionize AI models (sometimes called MLOps or AI platform engineering). In many organizations, AI infrastructure engineers are the backbone enabling data scientists and ML engineers to push models from research to production. This unique skill set – blending software engineering, cloud architecture, and ML knowledge – can make AI infrastructure experts as valuable as the model-builders. So which pays more? It depends on the context, but we’ll explore the numbers next.
Salary Breakdown by Experience Level (Entry, Mid, Senior)
Both AI infrastructure engineers and ML engineers earn well above average salaries from the start. However, data suggests AI-focused infrastructure roles might have a slight edge at certain experience levels. According to a 2025 salary guide, entry-level ML engineers (0–2 years experience) earn roughly $90,000–$130,000 in the US. Payscale data echoes this, putting entry-level ML engineer averages around $96,000 annually (with ranges from about $70K to $120K). In contrast, an entry-level AI engineer (often including infrastructure roles) averages about $130,548 per year in the US – considerably higher. This gap may reflect that many “AI infrastructure” positions require a bit more specialized experience or are found in top-paying companies.
By the mid-career level, ML engineer salaries climb fast. A mid-level ML engineer (3–7 years) can make around $130,000–$180,000. Glassdoor’s data shows a progression: an AI engineer with ~4–6 years experience earns about $138,000, and 7–9 years around $155,000. Refonte Learning’s analysis of tech salaries likewise found that senior AI engineers often see averages in the high $100Ks, in some cases around $200,000 for 6+ years experience. Senior ML engineers are not far behind – Glassdoor reports senior ML engineers around $156,797 base on average, and leadership roles (ML manager or director) hitting $200K+.
At the top end, both roles can command exceptional compensation, especially at elite firms. It’s not unheard of for a seasoned ML or AI infrastructure engineer at a top company to earn total packages in the mid-six figures. For instance, data indicates the average total compensation for ML/AI engineers in the US is about $247,000 when factoring in salary, bonuses, and equity. And some companies have blown past that: Glassdoor lists multiple employers where experienced ML engineers receive $300,000–$400,000 total pay. One example on the infrastructure side – at NVIDIA, an AI Infrastructure Engineer averages about $218K base salary + $49K additional pay (total ~$267K). Clearly, experience level and company pay scales matter greatly – a senior engineer at a big-tech or well-funded AI lab will earn far more than a junior engineer just starting out. But on average, data suggests AI infrastructure engineers have comparable if not slightly higher median salaries than ML engineers at similar experience levels, especially in AI-heavy industries.
Geography and Employer: Bay Area vs National, Big Tech vs Startups
Where you work and who you work for can swing the salary pendulum substantially. In general, major tech hubs in the U.S. pay a premium for both ML and AI infrastructure talent. Silicon Valley (San Francisco Bay Area), Seattle, and New York often offer 20–40% higher salaries than other region. For example, Glassdoor estimates a Machine Learning Engineer in San Francisco earns about $208,000 in total pay (with ~$148,000 base) – significantly above the national median of ~$155,000 total ( ~$120K base) for ML engineer. The same goes for AI infrastructure roles: California leads in demand. As of 2025, California hosts about 33% of AI job openings, and salaries there top the chart. A ZipRecruiter analysis put the average AI Infrastructure Engineer in California at roughly $125,000/year, but this likely skews low; many Bay Area AI infra roles pay well above that. In fact, Refonte Learning’s research highlights that tech-centric areas like Silicon Valley drive salaries to the highest tier, thanks to the concentration of companies and higher cost of living.
Employer type is another big factor. Big Tech firms (FAANG and similar) tend to offer the highest salaries, often bolstered by hefty stock packages and bonuses. According to a 2025 industry breakdown, a machine learning engineer at a FAANG company can earn $180,000–$250,000 on average, whereas at an AI startup the range might be $130,000–$190,000. The same trend applies to AI infrastructure engineers: a cloud giant or self-driving car company will pay more than a small startup or non-tech firm. Startups sometimes offer higher base pay than mid-size companies to attract talent (or significant equity), but rarely match the total comp of big corporations’ stock grants. As the LinkedIn salary report shows, even mid-sized tech companies (not top 5) typically pay ML engineers a healthy $150K–$200K range.
Outside the U.S., there are also notable differences. Global trends show U.S. salaries at the high end, with places like Europe or Asia offering lower averages for similar roles. For instance, an ML engineer in an emerging tech hub (Toronto, Berlin, etc.) might earn less than their U.S. counterpart – often due to lower local cost of living and different market demand Outsourcing regions (Eastern Europe, India, Southeast Asia) see significantly lower pay for AI expert. Still, within any region, the hierarchy remains: top tech employers and AI-focused industries pay the most. Financial firms and hedge funds can rival tech companies for ML talent (e.g. NYC hedge funds may pay $200K+ for ML role). Meanwhile, sectors like academia or government offer much lower compensation for similar skill sets. In summary, to maximize earnings in either role, aim for tech hub locations and high-paying industries – that’s where the gap between an average salary and a top-tier salary can easily double.
2024–2025 Compensation Trends and Outlook
The past two years have seen salaries surge for professionals in both AI infrastructure and machine learning. A recent analysis noted that AI engineer salaries jumped to ~$206,000 on average in 2025 – a $50K increase from the prior year This reflects how hot the AI field has become, especially with the rise of generative AI and large-scale machine learning models. Companies racing to develop AI (from chatbots to self-driving cars) are fiercely competing for talent, bidding up pay rates. Refonte Learning’s 2025 salary outlook highlights that roles like AI Engineers (which includes infrastructure specialists) are expected to see significant salary jumps due to AI and cloud. Similarly, machine learning engineer salaries remain on a steep upward trajectory, with some experts observing that 2023–2024 brought unprecedented offers (even approaching 7-figure total compensation for elite specialists at top. While those extreme cases are outliers, six-figure salaries have become the norm in these fields.
Another trend: blurring of role boundaries and upskilling. Many machine learning engineers are enhancing their skills in MLOps and distributed computing to make themselves more valuable – effectively taking on infrastructure engineering tasks. Conversely, AI infrastructure engineers often need a solid understanding of ML model lifecycles. This cross-pollination of skills can lead to higher pay. Employers highly value engineers who can “do it all,” from building a model to deploying it. Hybrid skill sets therefore command a premium. For example, an ML engineer who knows how to optimize GPU clusters or deploy models on Kubernetes might negotiate a higher salary because they save the company from needing a separate infrastructure hire.
Geopolitically, tech layoffs in late 2022/early 2023 caused a brief cooling in general software engineering pay, but AI roles were largely shielded. In fact, investments in AI infrastructure (think NVIDIA’s stock surge and cloud providers building AI supercomputers) mean infrastructure engineers with AI expertise are in high demand. Machine learning engineers also benefited from the generative AI boom – expertise in NLP and large language models became a golden ticket. All signs indicate that in 2025 and beyond, both roles will continue to be lucrative. Which pays more? In many settings their salaries overlap, but a skilled AI Infrastructure Engineer at a top firm can out-earn a typical ML Engineer, whereas a specialized ML Engineer in a lucrative domain (like finance or cutting-edge research) can out-earn many infrastructure folks. It often comes down to the individual’s niche and the company.
Bottom line: Both AI infrastructure engineers and ML engineers rank among the best-paid engineering roles in tech. If you have a passion for building AI systems, you really can’t go wrong with either path. In the next section, we’ll cover some tips to maximize your earnings in these careers. (SEO keywords: AI engineer salary 2025, ML engineer high-paying skills, AI job market trends 2024, generative AI demand, MLOps)
Actionable Tips to Maximize Your Earnings
Master In-Demand Skills: Develop expertise in the hottest skills for your role. For ML engineers, that might mean deep learning (LLMs, computer vision) and MLOps tools. For infrastructure engineers, focus on cloud platforms (AWS, GCP), container orchestration (Kubernetes), and distributed computing. Companies pay top dollar for those who can tackle cutting-edge challenges (Refonte Learning notes that professionals who stay ahead with AI and cloud skills “will command top-tier salaries”).
Choose High-Paying Domains: Aim for industries and employers known for generous compensation. Big Tech and finance consistently pay the most. Don’t be afraid to pursue roles at AI-driven startups as well – equity in a successful AI startup can be extremely valuable, and some startups offer above-market salaries for key hires.
Build a Portfolio and Reputation: Showcasing real projects can boost your market value. Employers love to see proven experience. For example, one Refonte Learning alum landed an ML Engineer role after building a portfolio of projects like chatbots and fraud detection models during training. Contribute to open-source AI infrastructure projects or publish your ML results – this can set you apart and justify a higher salary.
Negotiate and Know Your Worth: Stay informed about salary benchmarks (use resources like Glassdoor). When you get an offer, negotiate. Experienced AI/ML engineers can often leverage multiple offers or counteroffers – don’t leave money on the table. If you have rare skills (say, optimizing large-scale AI systems), highlight how that saves or earns the company.
Consider Location and Relocation: If possible, position yourself in a high-paying city or be open to remote roles based in those hubs. An engineer in San Francisco or Seattle will typically earn more than one in a smaller market. Some companies also offer cost-of-living adjustments or bonuses for critical talent willing to relocate. Just remember to weigh salary against living costs.
Conclusion & CTA
In the duel between AI infrastructure engineer and ML engineer salaries, there isn’t a one-size-fits-all winner – but both career paths are unequivocally lucrative. An AI infrastructure engineer might edge out in pay at a cloud computing giant, while a machine learning engineer could earn more at a hedge fund or in a specialized AI research role. Ultimately, the “which pays more” question depends on where you work, your experience, and your skill set. What’s clear is that as AI transforms industries, these roles are reaping the rewards. Employers are willing to invest heavily in talent that can build intelligent systems or the infrastructure that supports them.
If you’re planning a career in this space, focus on building expertise and real-world experience – the salaries will follow. Refonte Learning offers specialized programs in AI Engineering and Machine Learning that can help you gain those in-demand skills through hands-on projects and mentor guidance. Whether you choose the ML engineer route or the AI infrastructure path, staying at the cutting edge of AI technology is key to commanding top compensation. Ready to elevate your career in AI? Explore Refonte Learning’s courses and mentorship opportunities to get started on the path to your dream AI job.
FAQ
Q1: Do AI infrastructure engineers earn more than machine learning engineers?
There is a lot of overlap, and both roles have high salaries. On average, their pay is comparable. Some data shows AI infrastructure engineers earning slightly higher median salaries in tech, but a machine learning engineer in a top-paying industry can earn just as much or more. It really depends on the company and specialization rather than a strict role-based difference.
Q2: What exactly does an AI infrastructure engineer do compared to an ML engineer?
An AI infrastructure engineer builds and optimizes the platforms, tools, and hardware systems that allow AI models to run at scale. They handle things like data pipelines, model serving infrastructure, and cloud deployment. A machine learning engineer focuses more on developing and refining the ML models themselves – writing algorithms, training models, and integrating them into applications In practice, the two roles collaborate closely: ML engineers create the models, and AI infrastructure engineers ensure those models are efficiently deployed and maintained.
Q3: Why are salaries so high for these roles?
It comes down to supply and demand. Artificial intelligence is a booming field, and companies need skilled engineers to build AI-driven products. The work is highly specialized – requiring advanced technical knowledge – and the impact on business can be huge. For example, a well-implemented ML model can save a company millions or unlock new revenue, so they’re willing to pay premium salarie. Additionally, competition between tech firms (and industries like finance) for AI talent drives salaries up.
Q4: Does location really make a big difference in pay?
Absolutely. Engineers in Silicon Valley, Seattle, New York, and similar hubs often earn substantially more (20–40% higher) than those in regions with smaller tech. This is due to the concentration of big-budget companies and higher living costs in those areas. Many U.S. national “average” salary figures factor in a mix of high and lower cost regions. So if you’re based in the Bay Area or working remotely for a Bay Area company, expect the upper end of the salary range.
Q5: How can I increase my salary as an AI or ML engineer?
Beyond gaining experience, you can boost your earning potential by upskilling in high-demand areas (like learning new AI frameworks or cloud technologies), working on impactful projects that you can demonstrate to employers, and targeting roles in high-paying industries. Networking and continuous learning are important too – staying on top of emerging trends (like generative AI, MLOps, or AI ethics) can open doors to better oportunities. And of course, when an offer comes, negotiate confidently using market data to back up your ask.