Compiler Frontend Engineer

Normal is an applied AI company building foundational hardware and software for the semiconductor industry, critical AI infrastructure, and related systems. Normal's hardware runs the most memory-bound parts of large-model inference inside memory itself. As a Compiler Engineer focused on frontend and model bring-up, you will extend the compiler's IR, ops, and lowering passes so new LLM architectures are supported before customers request them. The role is research-adjacent: you are expected to follow the literature closely and publish on behalf of Normal. The chip is pre-silicon, so bring-up is validated against simulators and FPGA prototypes first.

Responsibilities

Model Bring-up: Bring up frontend and mid-level support for new LLM architectures in our compiler, from attention variants to MoE routing schemes; IR and Lowering: Extend our IR, ops, and lowering passes to represent new architectural primitives faithfully rather than approximately, with test coverage that keeps them correct as the compiler evolves; Quantization and Numerics: Represent the quantization schemes and numeric formats new models use, so lowering preserves model accuracy; Architecture Tracking: Track the LLM architecture landscape closely enough to recommend what the team brings up next; Cross-Team Handoffs: Hand lowered programs to the backend compiler engineers who map them onto our hardware, and work with our LLM serving engineer to take new models from compiler support to serving

Requirements

Hands-on ML compiler experience: you have written and shipped compiler passes; Deep familiarity with PyTorch internals, ideally including its compilation stack (TorchDynamo, FX, torch.export); Working knowledge of modern LLM architectures at the level of attention mechanisms, MoE routing, and quantization schemes; Patience for the unglamorous part of the job: getting a new architecture from "runs" to "runs correctly" to "runs correctly and fast"; Comfort designing compiler infrastructure in a young codebase as well as extending an established one; Comfort building software for hardware that is still evolving, from simulation through bring-up; Fluency in Python, and familiarity with C++ or Rust

Nice to have

Experience with other ML compilers, such as IREE, XLA, or TVM; Familiarity with other ML frameworks and graph formats, such as JAX or ONNX; Experience partitioning models across devices with tensor, pipeline, or expert parallelism; Experience with hardware-software co-design; Experience with non-standard or in-memory hardware targets; Contributions to open-source compiler or ML infrastructure projects