AWS Graviton4 (left) and AWS Trainium2
Tech giants are assembling AI supercomputers of unprecedented scale. Microsoft, Meta, Amazon, and Elon Musk’s xAI are building clusters boasting 100,000 or more H100 GPUs, with xAI planning to double its Colossus system to 200,000 GPUs — including the upcoming H200 model. To put this in perspective, OpenAI reportedly trained GPT-4 using roughly 25,000 A100 GPUs, a chip that offers only a small fraction of the H100’s power. But behind this massive buildout, a quieter yet equally intense battle is raging: the war for the specialized engineering talent capable of building and optimizing these AI behemoths.
A less visible but critical resource fuels this high-stakes competition for end users and AI chip companies: the scarce but highly sought-after engineers who can design and build the chips that power it all.
Late 2024 GPU and AI/ML talent market snapshot
Senior performance engineers
$180,000 – $340,000
ML profiling specialists
$270,000 – $420,000
GPU architecture roles
$175,000 – $315,000
Based on a dataset of more than 500 U.S.-based organizations. Data collected on November 25, 2024
Companies are willing to pay a premium for GPU expertise, with top positions commanding compensation packages well over $300,000 annually — although many fetch substantially less. At NVIDIA, senior GPU system software engineers can earn up to $339,250. At the same time, Apple offers up to $312,200 for GPU Register Transfer Level (RTL) design engineers based on an analysis of more than 500 public tech-focused job postings on November 25. (See average salaries for GPU job postings below.)
Amazon is also upping its hardware game through its subsidiary, Annapurna Labs. Its Trainium platform, introduced in 2021, is a custom-designed accelerator specifically designed for training deep learning models. Trainium2, which debuted in November 2023, offers up to four times faster training performance and three times more memory capacity than its predecessor, with improved energy efficiency to boot.
Bloomberg recently noted that the company is vying to loosen NVIDIA’s tight grip on the $100-billion-plus market for AI chips.
Microsoft and Alphabet are making similar moves to reduce their reliance on NVIDIA hardware, which remains in short supply.
A significant challenge of building performant GPUs is not just the design but optimizing performance. As the Bloomberg article noted, “checking that the switch-over didn’t break anything can eat up hundreds of hours of engineers’ time.” Performance engineers had the highest median compensation in the dataset of open GPU-related positions. At leading chip manufacturers and tech giants, positions such as principal solution engineers and ML profiling specialists can fetch salaries ranging from $270,000 to over $400,000. Even mid-level positions, such as GPU compiler performance engineers, can command packages in the $160,000 to $240,000 range.
The following chart shows salary ranges of a range of position types — including those not explicitly related to GPUs:
The x-axis labels of the image, “(min)” and “(max),” refer to the minimum and maximum salary ranges for each job category. For example, “System Architecture (min)” shows the lower end of the salary range for System Architecture positions, and “System Architecture (max)” shows the upper end of the salary range for System Architecture positions.
California and other hubs in Seattle and Austin remain prominent for GPU-related jobs. Santa Clara hosts the highest concentration of top-paying positions ranging from $175,000 to over $400,000 across hardware, software, and ML specialties. Here are the top cities in the dataset:
- Santa Clara, CA: Predominantly the hub for high-paying roles, especially for NVIDIA and Apple.
- Cupertino, CA: Significant presence of Apple roles.
- San Diego, CA: Key location for Qualcomm and Apple.
- Foster City, CA: Companies like Zoox have positions here.
- Folsom, CA: Intel is a major employer. Although it plans to sell its Folsom campus, it leases back a portion.
This is a review of data based on job posting sites filtered for the relevant roles pictured. The data is not comprehensive and does not cover the entire labor market.
Software-focused GPU positions commanded similar ranges from $117,000 to $339,250, with NVIDIA leading in system software and networking roles.
Amazon & Annapurna Labs Job Snapshot
System development engineer
$151,300 – $261,500/year
Senior SoC functional modeling engineer
$175,000 – $420,000/year
Sr. worldwide specialist, GenAI
$133,200 – $220,200/year
Source: https://www.amazon.jobs/
The broader category covering AI and ML positions showed the highest compensation bands, with NVIDIA’s principal and management roles in Visual AI and Deep Learning algorithms surpassing $400,000 for the maximum salary range. Even general GPU positions focusing on infrastructure and performance optimization ranged from $98,900 to $339,250, with senior DevOps and SRE roles for GPU clusters commanding top dollar.
The dataset also showed technical marketing positions specific to GPU and AI infrastructure, with competitive compensation between $120,000 and $276,000.
| Job Title | Average Salary in Job Postings |
|---|---|
| Graphics Power Analysis & Optimization Engineer | $228,840 |
| Graphics (GPU) Architectural Modeling Engineer | $176,225 |
| GPU Design Verification Engineer | $168,033 |
| Senior GPU System Software Engineer | $259,625 |
| GPU Compiler Engineer | $202,200 |
| GPU Research Engineer | $244,000 |
| GPU Compiler Performance Engineer | $202,200 |
| GPU Machine Learning Engineer | $123,600 |
| GPU Software Machine Learning Engineer | $123,600 |
Open job postings from Amazon and AWS reveal positions related to HPC frameworks and advanced AI/ML workloads. These positions focus on roles requiring GPU optimization, distributed computing, and large-scale machine learning systems expertise. Salaries for those positions range from $129,300 to $262,000.
The two companies with the highest number of open listings, NVIDIA and Apple, have job postings that reflect distinctly different GPU development strategies and priorities. NVIDIA’s listings skew toward AI/ML acceleration, high-performance computing, and developer ecosystem support, with multiple positions focused on GPU clusters, deep learning optimization, and graphics framework development. Conversely, Apple’s listings focus on power-efficient custom GPU architectures that are tightly integrated with its Apple Silicon platform. Open positions at Apple tend to emphasize mobile graphics performance, display technologies, and internal tooling. In essence, NVIDIA’s positions target broader market applications and third-party developers, whereas Apple’s roles concentrate on vertical integration within their hardware ecosystem. In addition, NVIDIA postings emphasize CUDA, deep learning frameworks, and high-performance computing experience, while Apple prioritizes experience with power optimization, mobile graphics, and system-on-chip integration.
