AMD · 4 hours ago
Software Development Engineer- SGLang and Inference Stack
AMD is a leading company in next-generation computing experiences, focusing on AI and data centers. The role involves optimizing and developing deep learning frameworks for AMD GPUs, enhancing performance, and collaborating with internal teams and open-source communities.
Embedded SoftwareArtificial Intelligence (AI)SemiconductorCloud ComputingElectronicsHardwareAI InfrastructureComputerEmbedded SystemsGPU
Responsibilities
Optimize Deep Learning Frameworks: Enhance performance of frameworks like TensorFlow, PyTorch, and SGLang on AMD GPUs via upstream contributions in open-source repositories
Develop and Optimize Deep Learning Models: Profile, analyze, code change and tune large-scale training and inference models for optimal performance on AMD hardware. Day-0 supports to many SOTA models, DeepSeek 3.2, Kimi K2.5, etc
GPU Kernel Development: Design, implement, and optimize high-performance GPU kernels using HIP, Triton, TileLang or other DSLs for AI operator efficiency
Collaborate with GPU Library and Compiler Teams: Work closely with internal compiler and GPU math library teams to integrate, optimize and align kernel-level optimizations with full-stack performance goals. Initiate and help with different level codegen optimizations
Contribute to SGLang Development: Support optimization, feature development, and scaling of the SGLang framework across AMD GPU platforms for LLM, multimodal serving and RL-training
Distributed System Optimization: Tune and scale performance across both multi-GPU (scale-up) and multi-node (scale-out) environments, including inference parallelism, prefill-decode disaggregation, Wide-EP and collective communication strategies
Graph Compiler Integration: Integrate and optimize runtime execution through graph compilers such as XLA, TorchDynamo, or custom pipelines
Open-Source Collaboration: Partner with external maintainers to understand framework needs, propose optimizations, and upstream contributions effectively
Apply Engineering Best Practices: Leverage modern software engineering practices in debugging, profiling, test-driven development, and CI/CD integration
Qualification
Required
Skilled engineer with strong technical and analytical expertise in GPGPU C++, Triton, TileLang or DSL development within Linux environments
Ability to thrive in both collaborative team settings and independent work
Ability to define goals, manage development efforts, and deliver high-quality solutions
Strong problem-solving skills
Proactive approach
Keen understanding of software engineering best practices
Bachelor's and/or Master's Degree in Computer Science, Computer Engineering, Electrical Engineering, Physics or a related field
Preferred
Proficient in C++ and/or Python (PyTorch, Triton, TileLang), with demonstrated ability to code, debug, profile, and optimize performance-critical code
Hands-on experience with SGLang or similar LLM inference frameworks
Background in compiler design or familiarity with technologies like LLVM, MLIR, or ROCm
Experience running and scaling workloads on large-scale, heterogeneous clusters (CPU + GPU) using distributed training or inference strategies
Experience contributing to or integrating optimizations into deep learning frameworks such as PyTorch, SGLang, vLLM, Slime, VeRL
Working knowledge of HIP, CUDA, Triton, TileLang or other GPU programming models; experience with GCN/CDNA architecture preferred
Benefits
AMD benefits at a glance.
Company
AMD
Advanced Micro Devices is a semiconductor company that designs and develops graphics units, processors, and media solutions.
H1B Sponsorship
AMD has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
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Trends of Total Sponsorships
2025 (836)
2024 (770)
2023 (551)
2022 (739)
2021 (519)
2020 (547)
Funding
Current Stage
Public CompanyTotal Funding
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OpenAIDaniel Loeb
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