Datacenter & Agentic AI Workload Performance Analysis Engineer

Who You Are

• You have a strong background in CPU performance analysis, workload characterization, or computer architecture, with experience connecting software behavior to hardware performance.
• You understand modern CPU microarchitecture, including superscalar pipelines, speculative execution, memory hierarchies, and vector/SIMD architectures.
• You enjoy digging into complex workloads, using profiling and simulation data to identify bottlenecks and turn analysis into actionable recommendations.
• You’re comfortable working across hardware and software, from CPU microarchitecture and RTL to operating systems, compilers, runtimes, and applications.
• You’re a strong technical communicator who enjoys collaborating with architects, designers, and software engineers on complex performance problems.

What We Need

• PhD in Computer Engineering, Electrical Engineering, Computer Science, or a related field, with strong research or industry experience in workload characterization, benchmark development, performance analysis, or simulation.
• Deep understanding of CPU architecture and RISC-V, including pipelines, speculative execution, vector/SIMD extensions, memory hierarchies, and performance tradeoffs.
• Hands-on experience with performance analysis and simulation tools such as Linux perf, strace, QEMU, or CPU microarchitecture simulators.
• Strong programming skills in C/C++, Python, Bash/Shell, and assembly or intrinsic programming, with experience working close to the hardware/software boundary.
• Strong understanding of systems software, including operating systems, virtualization, compilers, runtimes, and GNU/RISC-V software ecosystems.

What You Will Learn

• How real-world datacenter and agentic AI workloads influence CPU microarchitecture and architectural decisions.
• How to connect workload characterization and performance modeling to CPU design, RTL implementation, emulation, and silicon.
• How hardware/software co-design can improve CPU throughput, scalability, and performance-per-watt efficiency.
• How to analyze complex production workloads and reduce them into representative workloads and traces for architectural exploration.
• How emerging RISC-V capabilities, cloud infrastructure, compiler technology, and AI software stacks are shaping the future of high-performance.