AI Engineer, Recursive Self-Improvement for Compute
About The Position
We are hiring AI Engineers 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.
Requirements
- Strong software engineering experience in Python and at least one systems language such as C++, C, HIP, CUDA.
- Experience building AI, ML, agentic, optimization, or automation systems that are evaluated with objective metrics.
- Ability to design reliable experiment loops, benchmark harnesses, validation workflows, and correctness/performance evaluation pipelines.
- Strong technical judgment in debugging, profiling, root-cause analysis, and performance-oriented iteration.
- Clear communication skills and ability to work across AI research, hardware, software, and partner-facing teams.
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience.
Nice To Haves
- 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.
- Publications, open-source contributions, or shipped systems in AI systems, GPU computing, compilers, RL, or hardware/software co-design are a plus.
- Master's preferred; PhD is a plus, especially in AI systems, reinforcement learning, compilers, GPU computing, or hardware/software co-design.
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 patterns that can generalize across multiple compute and hardware optimization domains.
- Mentor other engineers and help set technical direction for self-improving AI systems for compute.
Benefits
- AMD benefits at a glance
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What This Job Offers
Job Type
Full-time
Career Level
Senior
