We are seeking a driven and curious Technical Intern to join our AI Software and Hardware Architecture team. In this role, you will work at the intersection of next-generation GPU architecture, performance simulation, and modern AI engineering.
Our team develops architectural models and virtual platforms (VPs) to project, evaluate, and optimize the performance of future high-performance computing (HPC) and AI GPU architectures before silicon is taped out. As part of this mission, you will actively integrate the latest generative AI capabilities-including GitHub Copilot, custom Large Language Model (LLM) agents, and developer tooling integrations-directly into our architectural modeling, automated testing, and performance analysis pipelines.
This internship offers an opportunity to gain hands-on experience with production-scale architectural simulators while pioneering modern, AI-augmen…
What you'd do
- Collaborate with senior architects to develop, calibrate, and validate cycle-approximate or cycle-accurate Virtual Platform (VP) models for future GPU architectures (compute engines, memory fabric, cache hierarchies, and interconnects).
- Implement functional and performance simulation components in modern C++ and SystemC/TLM frameworks.
- Assist in porting, configuring, and executing graphics and compute workloads (e.g., PyTorch, SYCL, OpenCL, Vulkan/DirectX) across simulated environments.
- Accelerate development and debugging efficiency by leveraging GitHub Copilot and state-of-the-art LLM capabilities across team repositories.
- Prototype and deploy agentic AI workflows and tools (e.g., Model Context Protocol [MCP] integrations, automated log and trace summarization, error diagnosis).
- Build AI-assisted test generation tools and validation scripts using Python and unit testing frameworks (e.g., GoogleTest).
- Collect, profile, and analyze trace-driven simulation data to pinpoint memory subsystem bottlenecks, cache miss penalties, and pipeline stalls.
- Create automated visualization dashboards and reporting scripts to present performance trade-offs to senior architecture and design teams.
- Investigate anomaly detection in large-scale simulation telemetry using machine learning and statistical analysis techniques.
What they want
- Currently enrolled in an accredited Master's or Ph.D. program in Computer Science, Computer Engineering, Electrical Engineering, or a closely related discipline.
- Strong proficiency in C++ (modern C++17/20 preferred) and Python.
- Solid foundational knowledge of computer architecture principles (cache hierarchies, memory coherence, pipelining, and vector/SIMD processing).
- Experience with Linux/Unix environments, version control systems (Git), and modern build pipelines (CMake, Ninja).
- Familiarity with utilizing AI developer tooling (e.g., GitHub Copilot, Anthropic APIs, or local LLM runtimes) for software engineering.
What you get
- Mentorship from industry-leading Principal Engineers and Architects in GPU architecture and AI system design.
- Exposure to pre-silicon hardware validation methodologies used across world-class compute products.
- An innovative environment where you are encouraged to experiment with and deploy the latest generative AI paradigms into traditional systems engineering.
- Competitive intern compensation, technical networking events, and career development sessions
- Starts
- 2026-10-01