AI Engineer, Recursive Self-Improvement for Compute

What you'll do

• Build agentic and learning-driven optimization loops for compute workloads and hardware engineering workflows.
• Develop automated systems to generate, compile, test, benchmark, and profile candidate improvements with minimal human intervention.
• Convert complex engineering tasks into verifiable workflows with clear graders, metrics, and failure feedback.
• Collaborate with AI researchers on reward design, reward shaping, and model improvement loops.
• Design feedback systems that collect data from successful/failed attempts, profiler traces, and benchmark results.
• Improve iteration speed through staged validation, caching, parallel execution, and faster feedback paths.
• Build reusable tools and patterns that generalize across multiple compute and hardware optimization domains.
• Mentor other engineers and set technical direction for self-improving AI systems for compute.

What we're looking for

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related field.
• Master's degree preferred and PhD considered a plus in fields like AI systems, RL, compilers, or GPU computing.
• Proficiency 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 with GPU kernels, ROCm/HIP, CUDA, Triton, PyTorch, JAX, 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.
• Experience building production-quality evaluation platforms, experiment tracking, dashboards, or leaderboards.