AI Engineer

AMD

HybridPosted July 29, 2026

About the role

AI Engineer at AMD to build recursive self-improvement systems for compute. This role sits at the intersection of AI systems, performance engineering, hardware-aware optimization, and agentic software development. You will help build systems where AI proposes improvements, verifies correctness, measures impact, learns from failures, and improves the next generation of compute workloads and platforms. The work requires turning complex engineering tasks into repeatable optimization loops with clear inputs, candidate generation, automated validation, measurable scoring, and reliable iteration. You will work on problems where feedback may be expensive, correctness is non-negotiable, and small improvements can have large impact at scale.

AI Engineer

AMD

HybridPosted July 29, 2026

About the role

AI Engineer at AMD to build recursive self-improvement systems for compute. This role sits at the intersection of AI systems, performance engineering, hardware-aware optimization, and agentic software development. You will help build systems where AI proposes improvements, verifies correctness, measures impact, learns from failures, and improves the next generation of compute workloads and platforms. The work requires turning complex engineering tasks into repeatable optimization loops with clear inputs, candidate generation, automated validation, measurable scoring, and reliable iteration. You will work on problems where feedback may be expensive, correctness is non-negotiable, and small improvements can have large impact at scale.

Responsibilities

Build agentic and learning-driven optimization loops for compute workloads and hardware engineering workflows.
Develop systems that generate, compile, test, benchmark, profile, and iterate on candidate improvements with minimal human intervention.
Convert high-value engineering workflows into verifiable tasks with clear graders, harnesses, metrics, and failure feedback.
Collaborate with AI researchers on reward design, reward shaping, reward hacking analysis, long-horizon optimization, and model improvement loops.
Design feedback systems that accumulate useful data from successful attempts, failed attempts, profiler traces, benchmark results, and validation logs.
Improve iteration speed through staged validation, caching, parallel execution, proxy metrics, and faster feedback paths.
Build reusable tools and patte

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