AI Engineer, Recursive Self-Improvement for Compute

Job Title

AI Engineer, Recursive Self-Improvement for Compute

Role Summary

Build and operate systems that enable recursive self-improvement for compute workloads and hardware engineering workflows. The role sits at the intersection of AI systems, performance engineering, hardware-aware optimization, and agentic software development.

The primary mission is to convert complex engineering tasks into repeatable, measurable optimization loops that propose, validate, score, and iterate on candidate improvements.

Experience Level

Mid-level. No explicit years-of-experience specified.

Responsibilities

Hands-on engineering to design, build, and maintain automated optimization and validation loops for compute and hardware workflows.

• Build agentic, learning-driven optimization loops for compute workloads and hardware engineering workflows.
• Develop systems to generate, compile, test, benchmark, profile, and iterate on candidate improvements with minimal human intervention.
• Convert high-value engineering workflows into verifiable tasks with graders, harnesses, metrics, and failure feedback.
• Collaborate with AI researchers on reward design, long-horizon optimization, and evaluation of model-driven improvements.
• Design feedback systems that collect data from successful and failed attempts, profiler traces, benchmark results, and validation logs.
• Improve iteration speed via staged validation, caching, parallel execution, proxy metrics, and faster feedback paths.
• Build reusable tools and patterns that generalize across compute and hardware optimization domains.
• Mentor engineers and help set technical direction for self-improving AI systems for compute.

Requirements

Core technical skills and practical experience required to deliver production-quality experiment and optimization loops.

Must-have:

• Strong software engineering in Python and at least one systems language (C++, C, HIP, or CUDA).
• Experience building AI/ML, agentic, optimization, or automation systems evaluated with objective metrics.
• Ability to design reliable experiment loops, benchmark harnesses, validation workflows, and correctness/performance evaluation pipelines.
• Strong debugging, profiling, root-cause analysis, and performance-oriented iteration skills.
• Clear communication and experience collaborating across research, hardware, software, and partner-facing teams.

Nice-to-have:

• Experience with GPU kernels and tooling (ROCm/HIP, CUDA, Triton) and ML frameworks (PyTorch, JAX, TensorFlow).
• Experience with reinforcement learning, 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, or hardware/software co-design.
• Experience building production evaluation platforms, experiment tracking, dashboards, or leaderboards; publications or open-source contributions are a plus.

Education Requirements

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related field, or equivalent practical experience. Master's preferred; PhD is a plus, especially in AI systems, reinforcement learning, compilers, GPU computing, or hardware/software co-design.

About the Company

Company: Advanced Micro Devices

Headquarters: Sunnyvale, California, USA

Advanced Micro Devices, or AMD, is a global semiconductor company that designs and manufactures microprocessors, graphics processors, and related technologies for a variety of computing devices. Known for pushing the boundaries of innovation, AMD's mission is to deliver high-performance computing solutions for AI, data centers, gaming, and embedded applications. They foster a collaborative, inclusive culture focused on creativity and problem-solving, aiming to drive progress and excellence in technology.