AI Compute Systems Engineer, up to Staff

Bachelor's degree in Science, Engineering, or related field and 4+ years of ASIC design, verification, validation, integration, or related work experience. OR Master's degree in Science, Engineering, or related field and 3+ years of ASIC design, verification, validation, integration, or related work experience. OR PhD in Science, Engineering, or related field and 2+ years of ASIC design, verification, validation, integration, or related work experience. The engineer will bridge AI workload behavior with process technology and chiplet/3D integration strategies. - Strong understanding of AI use cases and system-level KPI dependencies. - Basic programming and modeling skills (Python or similar). - Master's or PhD in EE/CE/CS or related field. - 3-8+ years of AI compute, system architecture, or related experience. - Ability to operate effectively in exploratory R&D environments. - Analyze AI workloads and their dependencies on system KPIs (power, latency, bandwidth). - Build modeling frameworks (e.g., Python) to evaluate how 3D/heterogeneous architectures impact compute efficiency. - Collaborate with architecture, systems, and process teams to map AI requirements to chip integration and technology roadmaps. - Study the interaction among CPU, GPU, NPU and other IP blocks within end‑to‑end AI pipelines. - Explore trends in AI compute architectures, accelerators, and chip‑integration strategies.