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Advanced AI Workloads - Performance and Scalability Engineer
AMD · San Jose, CA · Posted 2 days ago
Own end-to-end inference performance on AMD GPUs for strategic customer engagements, leading a small technical team and leaving reusable optimization methodology behind.
AMD is looking for a performance-obsessed engineer to drive AI inference performance to the absolute limit on AMD GPUs. You will lead a small, highly technical team and work end-to-end across the stack: profiling, diagnosing, and optimizing leading models on customer-relevant serving configurations (e.g. agentic coding, long-context, high-throughput serving). You move from challenge to challenge, tackling the hardest performance problems across our most strategic customer engagements and leaving behind measurable uplifts and reusable methodology. This is not a sustaining role: every engagement is different, every optimization leaves a lasting impact.
You can take any AI workload, understand it top to bottom, and make it faster. You are equally comfortable profiling a distributed serving deployment, diagnosing a kernel-level bottleneck, and presenting optimization results to a customer's VP of Engineering. You understand GPU kernel performance deeply: not just how to use profiling tools, but how to reason about occupancy, cache behavior, memory coalescing, and instruction-level bottlenecks from first principles. You lead through technical depth: you set the standard for your team by doing the hardest work yourself and pulling others up along the way. You are AI-fluent, not just in the models you optimize, but in how you work: you leverage AI agents and tools daily to accelerate your workflows, and you actively define new ways of using them to make yourself and your team more effective. You thrive under pressure, move fast, and measure everything.
KEY RESPONSIBILITIES:
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This posting is sourced from AMD's own career site — applying takes you there directly.
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There are 1,335 open roles at AMD right now. This role centers on Gpu Kernel Optimization, Vllm, Sglang, and Tensorrt-llm.
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