AI Engineer, Recursive Self-Improvement for Compute - CA, Santa Clara, United States

Originally posted 8 July 2026 by the employer.

About the role

AMD is hiring an AI Engineer to build recursive self-improvement systems for compute workloads and platforms. This role focuses on the intersection of AI systems, performance engineering, hardware-aware optimization, and agentic software development.

What you'll do

- 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.

What you'll need

- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience. - Software engineering experience in Python and at least one systems language such as C++, C, HIP, or CUDA. - Experience building AI, ML, agentic, optimization, or automation systems evaluated with objective metrics. - Experience designing experiment loops, benchmark harnesses, validation workflows, and correctness/performance evaluation pipelines. - Technical background in debugging, profiling, root-cause analysis, and performance-oriented iteration.

Nice to have

- Experience with GPU kernels, ROCm/HIP, CUDA, Triton, PyTorch, JAX, TensorFlow, or distributed training/inference systems. - Experience with reinforcement learning, post-training, reward modeling, automated program optimization, or agentic coding systems. - Familiarity with CPU performance engineering, compiler optimization, benchmarking, profiling, or math libraries. - Exposure to hardware design, simulation, formal verification, performance/power/area analysis, or hardware/software co-design. - Experience building production-quality evaluation platforms, experiment tracking, dashboards, or leaderboards. - Master's degree or PhD in AI systems, reinforcement learning, compilers, GPU computing, or hardware/software co-design.

Skills: AI-for-engineering, recursive self-improvement, hardware-aware optimization, agentic software development, GPU/CPU performance, ROCm/HIP