AI Engineer, Recursive Self-Improvement for Compute

About the role

The Role & The Person 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.

Key Responsibilities

• Build agentic and learning-driven optimization loops for compute workloads and hardware engineering workflows
• Develop automated systems that generate, compile, test, benchmark, profile, and iterate on candidate improvements with minimal human intervention
• 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

Technical Focus Areas

• AI-assisted program optimization, code generation, debugging, and automated repair
• GPU and CPU performance engineering, including kernels, libraries, compilers, profilers, and benchmark-driven optimization
• Agentic workflows that use tools, tests, simulators, profilers, and structured feedback to improve over time
• Reinforcement learning, post-training, reward modeling, and evaluation methods for engineering tasks
• Hardware-aware optimization where correctness, latency, throughput, area, power, timing, or resource usage may all matter
• Experiment platforms, leaderboards, dashboards, and data pipelines for reproducible comparison and continuous improvement

Qualifications & Experience

• Education: 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
• Required Skills: Strong 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 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

Preferred Experience

• 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