GPU Inference Performance Engineer — Hybrid Dublin

TensorX; Dublin; Hybrid; EUR 90,000 - 130,000; Full time

Benefits offered by this job

25 days paid annual leave

Hybrid working in Dublin

Free inference tokens!

Job summary

TensorX, based in Dublin, is seeking GPU Performance Engineers to optimize inference engines, patch open-source components, and push improvements across a live fleet. You will work on kernel-level performance, model bring-up, and cache behavior to meet tight latency targets in a high-concurrency environment.

Reporting to the CTO, you’ll patch defects in SGLang and related tools, verify fixes on production traffic, and collaborate with the Inference Team to size pools and improve throughput.

Qualifications

• Read inference engine source to identify mechanism behind problems.
• Solid understanding of GPU architecture and CUDA fundamentals.
• Working knowledge of transformer inference, attention variants, KV cache and quantisation.
• Proficiency in Python and comfortable reading C++/CUDA.
• Measure and report results with clear trade-offs; test timing and correctness.

Responsibilities

• Engine tickets end to end: reproduce, diagnose, patch, verify, and roll out fixes.
• Investigation & diagnosis across GPU memory, engine scheduler, container, router, and traffic shape.
• Improve Goodput per GPU by locating bottlenecks in kernels, scheduler, or configuration.
• Patch SGLang and upstream defects; keep image patch sets consistent across fleet.
• Bring up new models on day zero; test layouts and caching strategies.
• Document findings with repeatable benchmarks for the team and customers.

Skills

GPU architecture

CUDA fundamentals

Transformer inference

Python

C++ & CUDA

Problem solving

Communication

Open-source patching

Education

BSc/MSc/PhD in CS/Engineering/ML

Tools

SGLang

vLLM

TensorRT-LLM

Job description

TensorX, based in Dublin, is seeking GPU Performance Engineers to optimize inference engines, patch open-source components, and push improvements across a live fleet. You will work on kernel-level performance, model bring-up, and cache behavior to meet tight latency targets in a high-concurrency environment.

Reporting to the CTO, you’ll patch defects in SGLang and related tools, verify fixes on production traffic, and collaborate with the Inference Team to size pools and improve throughput.