For years, CUDA engineers who optimize Nvidia chips have been among tech’s most sought-after specialists. Now, they’re increasingly managing AI that does it for them.
These engineers specialize in using Compute Unified Device Architecture (CUDA) — Nvidia’s software for programming its AI chips, known as GPUs — to write code that can run AI as efficiently as possible.
The jobs are coveted because compute costs are massive; engineers who can squeeze the most value from a chip can save companies millions.
CUDA engineers traditionally spent their days writing kernels — the small pieces of code that tell a GPU how to do a single job as quickly as possible — and then testing them to find the fastest version. Now, AI is taking over much of that painstaking work by generating hundreds of kernels, testing them, and picking the best one.
As a result, CUDA engineers increasingly spend their time supervising coding agents. It’s part of a broader shift across the software industry, where developers are moving from writing code to managing AI.
This involves setting goals, checking results, and stepping in when the AI gets stuck, said Jeremy Nixon, founder of the startup Infinity, which builds AI that optimizes chip software.
Because AI can introduce “bizarre” bugs that humans wouldn’t have written, the job of reviewing AI code has become “more intense,” said Anne Ouyang, cofounder of the AI infrastructure startup Standard Kernel.
Despite the shift, hiring data suggests demand for CUDA engineers remains strong. While overall demand for software engineers is lower than in 2023, “demand for some specialized skills, like CUDA, has grown,” said Elena Magrini, head of global research at the labor market analytics firm Lightcast.
And scarcity remains a challenge, even as AI has supercharged productivity, said Bing Xu, founder of the AI optimization startup INT21. That’s because the deepest CUDA expertise was built over more than 20 years, long before AI sent demand soaring.
“In the past, we couldn’t hire enough good-quality CUDA engineers, and now AI is filling the gap,” Xu said.
CUDA engineer jobs are evolving
CUDA engineering roles exist both inside and outside Nvidia — including at AI labs and cloud giants. There have already been more US job postings requiring CUDA skills through the first eight months of 2026 than during all of 2025, per Lightcast.
Nvidia remains the largest employer hiring for these positions, according to the firm, with over 300 active US job postings requiring CUDA skills as of September. Public Nvidia job postings for CUDA-related engineering roles advertise base salaries as high as $431,250 before stock and benefits.
Nixon said that AI lowers the barrier to entry for engineers with less experience while simultaneously pushing veterans toward higher-level work, like overseeing AI.
Ouyang said AI could hit junior CUDA engineers hardest, while specialists who can outperform AI and verify its work will become more valuable.
And over time, AI could take on a greater role.
Xu said his company’s research had already shown that AI can outperform human engineers on certain benchmarks for writing kernels.
In some cases, AI already writes CUDA code that engineers can’t fully understand, though they can verify it’s correct, Nixon said.
He said this ability to outperform human engineers offers an early glimpse of “superhuman” AI in the real world.
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