AI Systems / Compute Architect

About

A fast-growing semiconductor startup is developing a next-generation AI acceleration platform focused on dramatically improving efficiency for modern machine learning workloads, particularly large-scale inference. This role will play a key part in defining both the silicon architecture and the broader system stack, with an emphasis on tightly integrated compute, memory, and interconnect.

What You’ll Do

• Define and architect end-to-end compute systems for AI/ML workloads, including hardware requirements and system-level topology.
• Work cross-functionally with architecture, design, and external IP teams to drive development from early concept through production.
• Optimize system performance by balancing compute, memory bandwidth, and interconnect efficiency to improve throughput, latency, and power.
• Collaborate with software, compiler, and ML teams to enable scalable deployment of AI models through effective abstractions and tooling.
• Lead architectural exploration and performance modeling to evaluate tradeoffs and guide key design decisions.
• Stay current with advancements in AI models, compute architectures, and emerging optimization techniques.

What You’ll Bring

• Strong background in modern compute architectures, including CPUs, GPUs, and specialized accelerators.
• Solid understanding of machine learning workloads, particularly transformer-based models and large-scale inference techniques.
• Experience evaluating hardware tradeoffs across power, performance, and area (PPA).
• Familiarity with architectural simulation and performance analysis tools.
• Proficiency in C/C++ and Python for modeling, simulation, or system-level development.
• Exposure to ML frameworks such as PyTorch, JAX, or similar is a plus.