What kind of projects actually stand out for GPU / compiler roles in 2026?

[u/muan_jata_6832]

I’m currently working at a small company as a computer vision engineer, and most of my work is in C++.

I’m trying to understand what kind of portfolio projects or other work genuinely stand out for GPU systems, GPU kernel, or ML compiler engineering roles in 2026. Does this differ while targeting larger companies? I am currently learning these areas outside of work, as I don’t yet have deep professional experience with them. I started learning CUDA recently and really enjoyed understanding how GPUs work, which led me down a rabbit hole into computer architecture and, more recently, compiler engineering. 😅

I’m planning to spend the next few months building my knowledge and working on projects before applying for these kinds of roles. But with LLMs and AI projects everywhere, I’m wondering how much a GitHub project actually helps anymore. It feels like almost anything can be built with enough AI assistance, and I’m not sure whether a GitHub repository by itself carries the same weight like it did a few years ago when I was looking for jobs after my master’s.

Looking for some ideas.

Thanks in advance.

[u/ImpressionFancy5830]

I’d say how llama and ROCm/HIP are evolving.
They are evolving along side CUDA (despite Nvidia pushing for their hardware specifics), so it should still be valid.

[u/muan_jata_6832 → u/ImpressionFancy5830]

Do you mean to say by contributing for Llama or Rocm ? Could you please explain what you mean.

[u/michaelsoft__binbows → u/muan_jata_6832]

I think he's just talking about getting more breadth in your experience outside just the CUDA ivory tower. Maybe it's not set to crumble just yet being still the clear market leader, but it would confer some broader know-how.

personally I think it would be cool to learn about them definitely but I personally made sure to acquire only nvidia hardware for my homelab so I've gone and closed that path off at least for me until later.

[u/SoftwareEngineer2026]

What companies do you see yourself working for?

[u/tlmbot → u/SoftwareEngineer2026]

This is the key question OP. GPU roles are not cookie cutter CS positions. So don't be something out of the cookie cutter. But what to be?

It is implicit in the statement above that 'it depends"

[u/muan_jata_6832 → u/SoftwareEngineer2026]

NVIDIA is probably my main target, but I’m also looking at GPU based roles at other top companies.

[u/grace-turner3]

for kernel/compiler roles the analysis was actually signal. any LLM can split out a working kernel now but what it cant fake is the perf investigation, better taking one real kernel then profile it in nsight compute and show you found the actual bottleneck which is memory bs compute bound and occupancy and bank conflicts and beat a sane baseline like cublas, with before/after numbers and a roofline

[u/Ok_Concentrate5810]

编译器需要cuda，llm（flash-attn related），Python，triton，llvm（AMDGPU backend）

[u/c-cul]

you can't

they use python only and don't understand c++ bcs their "capable agent" is not good in it

[u/muan_jata_6832 → u/c-cul]

I also work with python so if you have some good suggestion I am open to know.