Senior LLM Inference Performance Engineer

AMD; Helsinki; On-site; EUR 90,000 - 150,000; Full time

Job summary

AMD is looking for a performance-obsessed engineer to drive AI inference performance to the absolute limit on AMD GPUs, with SGLang as the primary serving framework. You will lead a small, highly technical team and work end-to-end across the stack: profiling, diagnosing, and optimizing leading models running on SGLang across customer-relevant serving configurations (e.g.

agentic coding, long-context, high-throughput serving).

Qualifications

• 7+ years of software development experience in GPU computing or AI systems.
• Hands-on experience with SGLang internals; familiarity with vLLM or TensorRT-LLM is a plus.
• Strong workload profiling and bottleneck diagnosis skills.
• Understanding of GPU kernel performance characteristics and model architectures.

Responsibilities

• Drive performance optimization end-to-end on SGLang across models and configurations.
• Profile, diagnose, and resolve hardest cross-stack bottlenecks in SGLang deployments.
• Diagnose kernel-level issues and translate findings into optimizations.
• Lead customer-facing technical engagements and present measurable uplifts.
• Integrate and optimize custom kernels within SGLang (HIP, CUDA, Triton, CK, PyDSL, ASM, AITER).
• Optimize multi-node distributed inference and scale-out performance.
• Develop and refine shared performance optimization methodologies.
• Leverage AI agents to accelerate daily work and define best practices.
• Contribute upstream to SGLang and open-source frameworks such as vLLM and PyTorch.

Skills

Python

C++

Linux systems

Customer-facing leadership

GPU computing

Profiling

English communication

Education

Master's degree in Computer Science/Engineering

PhD preferred

Tools

HIP

CUDA

Triton

CK

Gluon

PyDSL

ASM

AITER

RCCL/NCCL

RDMA

Job description

AMD is looking for a performance-obsessed engineer to drive AI inference performance to the absolute limit on AMD GPUs, with SGLang as the primary serving framework. You will lead a small, highly technical team and work end-to-end across the stack: profiling, diagnosing, and optimizing leading models running on SGLang across customer-relevant serving configurations (e.g.

agentic coding, long-context, high-throughput serving).