Our diverse team of technologists have developed a high performance RISC-V-based CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities.

We are looking for a talented engineer to build and run the performance infrastructure shared by our RISC-V Software and RISC-V Performance teams. This is the plumbing that both teams' performance work stands on — benchmarking automation, data collection, and workload capture across silicon, FPGA/emulation platforms and performance models. You'll work across both teams, enabling performance and software engineers to spend their time on analysis and optimization instead of running experiments by hand.

This role is hybrid, based out of Austin, TX or Santa Clara, CA.

We welcome candidates at various experience levels. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who You Are

• Great at identifying problems and developing solutions, with a bias toward owning the systems you build.
• Enjoys building tools and optimizing workflows so other engineers can move faster.
• Strong Linux systems engineer, comfortable wrangling lab machines, clusters and flaky hardware platforms.
• Skilled in Python and shell scripting, with solid CI (GitLab) and container experience.
• Experienced automating at scale — job scheduling, artifact management, databases and dashboarding; curiosity about CPU performance is a plus, deep microarchitecture background is not required.

What We Need

• Build and maintain benchmarking automation and CI pipelines that run SPEC and real-world workloads across RISC-V silicon, FPGA/emulation platforms and performance models.
• Own SimPoint generation and management infrastructure.
• Build performance-data collection into databases, with dashboards and visualization tools on top, so trends and regressions are visible without manual effort.
• Develop workload capture and replay tooling (QEMU- and CRIU-based checkpointing) so perf engineers can trace and analyze workloads reproducibly.
• Collaborate closely with performance and software engineers on both teams to understand their flows and remove the infrastructure friction from them.

What You Will Learn

• End-to-end exposure to CPU performance work, from workload capture through analysis and convergence.
• Hands-on experience with pre-silicon and post-silicon performance platforms — models, emulation, FPGA and real RISC-V hardware.
• Integration of open-source and industry-standard tools into production-grade flows.
• Work in a deeply technical, highly collaborative team solving cutting-edge CPU and AI challenges.