Principal AI Performance Engineer, LLM Inference
Real job — pulled straight from AMD’s careers page · Verified July 12, 2026 · No reposts.
Job description
AMD is hiring a Principal AI Performance Engineer, LLM Inference — a full-time, based in Helsinki, Finland role. Apply directly on AMD's careers page below.
WHAT YOU DO AT AMD CHANGES EVERYTHING
At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.
- Drive performance optimization end-to-end on SGLang across leading models and customer-relevant serving configurations, closing competitive gaps through kernel and systems-level optimizations
- Profile, diagnose, and resolve the hardest cross-stack performance bottlenecks in SGLang deployments, from GPU kernels and operator dispatch to the SGLang scheduler, RadixAttention/prefix caching, and multi-node communication
- Diagnose kernel-level performance issues using profiling tools: identify occupancy limitations, L2 cache thrashing, register pressure, memory coalescing issues, etc, and translate findings into actionable optimizations
- Lead customer-facing technical engagements: present findings, recommend optimizations, and deliver measurable performance uplifts on SGLang
- Integrate and optimize custom kernels (Triton, Gluon, CK, PyDSL, ASM, AITER) within SGLang, understanding dispatch paths, shape extraction, and backend selection
- Optimize multi-node distributed inference on SGLang: communication-compute overlap, parallelism strategies (TP/EP/DP), and scale-out performance
- Develop and refine shared performance optimization methodology that raises the bar across the broader team
- Leverage AI agents to accelerate daily work and define best practices for AI-assisted performance engineering
- Upstream optimizations into SGLang and adjacent open-source frameworks such as vLLM and PyTorch
- 7+ years of software development experience in GPU computing, AI systems, or high-performance computing
- Deep hands-on experience with SGLang internals; familiarity with vLLM, TensorRT-LLM, or similar is a plus
- Strong background in end-to-end workload profiling and bottleneck diagnosis: you can trace from user request through the SGLang runtime to the GPU kernel and back
- Understanding of GPU kernel performance characteristics: occupancy, register and LDS pressure, memory coalescing, cache utilization, wavefront scheduling, and instruction-level bottlenecks
- Ability to read and reason about kernel-level profiling data and translate it into concrete optimization actions. You may not write kernels from scratch daily, but you can tell exactly why one is slow and what needs to change
- Understanding of model architectures (transformers, MoE, diffusion), inference paradigms (speculative decoding, prefill-decode disaggregation, continuous batching), and how they map to hardware and to SGLang's execution model
- Experience with custom kernel development or integration (HIP, CUDA, Triton, CK, or similar)
- Understanding of multi-GPU and multi-node distributed systems: scale-up and scale-out topologies, RCCL/NCCL, RDMA, and communication-compute overlap
- System and rack-level design awareness: understanding performance tradeoffs across the full deployment stack
- Strong proficiency in Python and C++
- Customer-facing technical leadership experience: ability to engage with customers, present findings, and drive decisions
- Fluent in AI-assisted development: daily user of AI agents and tools, with a mindset toward defining new AI-powered workflows
- Strong Linux systems knowledge
- Excellent written and verbal English communication skills
#LI-MH3
#LI-HYBRID
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
- Drive performance optimization end-to-end on SGLang across leading models and customer-relevant serving configurations, closing competitive gaps through kernel and systems-level optimizations
- Profile, diagnose, and resolve the hardest cross-stack performance bottlenecks in SGLang deployments, from GPU kernels and operator dispatch to the SGLang scheduler, RadixAttention/prefix caching, and multi-node communication
- Diagnose kernel-level performance issues using profiling tools: identify occupancy limitations, L2 cache thrashing, register pressure, memory coalescing issues, etc, and translate findings into actionable optimizations
- Lead customer-facing technical engagements: present findings, recommend optimizations, and deliver measurable performance uplifts on SGLang
- Integrate and optimize custom kernels (Triton, Gluon, CK, PyDSL, ASM, AITER) within SGLang, understanding dispatch paths, shape extraction, and backend selection
- Optimize multi-node distributed inference on SGLang: communication-compute overlap, parallelism strategies (TP/EP/DP), and scale-out performance
- Develop and refine shared performance optimization methodology that raises the bar across the broader team
- Leverage AI agents to accelerate daily work and define best practices for AI-assisted performance engineering
- Upstream optimizations into SGLang and adjacent open-source frameworks such as vLLM and PyTorch
- 7+ years of software development experience in GPU computing, AI systems, or high-performance computing
- Deep hands-on experience with SGLang internals; familiarity with vLLM, TensorRT-LLM, or similar is a plus
- Strong background in end-to-end workload profiling and bottleneck diagnosis: you can trace from user request through the SGLang runtime to the GPU kernel and back
- Understanding of GPU kernel performance characteristics: occupancy, register and LDS pressure, memory coalescing, cache utilization, wavefront scheduling, and instruction-level bottlenecks
- Ability to read and reason about kernel-level profiling data and translate it into concrete optimization actions. You may not write kernels from scratch daily, but you can tell exactly why one is slow and what needs to change
- Understanding of model architectures (transformers, MoE, diffusion), inference paradigms (speculative decoding, prefill-decode disaggregation, continuous batching), and how they map to hardware and to SGLang's execution model
- Experience with custom kernel development or integration (HIP, CUDA, Triton, CK, or similar)
- Understanding of multi-GPU and multi-node distributed systems: scale-up and scale-out topologies, RCCL/NCCL, RDMA, and communication-compute overlap
- System and rack-level design awareness: understanding performance tradeoffs across the full deployment stack
- Strong proficiency in Python and C++
- Customer-facing technical leadership experience: ability to engage with customers, present findings, and drive decisions
- Fluent in AI-assisted development: daily user of AI agents and tools, with a mindset toward defining new AI-powered workflows
- Strong Linux systems knowledge
- Excellent written and verbal English communication skills
#LI-MH3
#LI-HYBRID
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
Tags: No, EUR €99,050.00/Yr., EUR €141,500.00/Yr., Global Careers (do not use for US or Canada)
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Frequently asked questions
What skills are required for Principal AI Performance Engineer, LLM Inference at AMD?
The required skills for Principal AI Performance Engineer, LLM Inference at AMD include: Python, C++, Linux, AI, Machine Learning, LLM, PyTorch.
What is the seniority level for Principal AI Performance Engineer, LLM Inference at AMD?
Principal AI Performance Engineer, LLM Inference at AMD is a Principal / Manager level position.
How do I apply for Principal AI Performance Engineer, LLM Inference at AMD?
You can view the full description and apply for Principal AI Performance Engineer, LLM Inference at AMD on EchoJobs: https://echojobs.io/job/amd-principal-ai-performance-engineer-llm-inference-sglang-70uvu.