Compiler Frontend Engineer

Digitailor; Palo Alto (CA); On-site; USD 140,000 - 230,000; Full time

Job summary

Normal Computing in Silicon Valley builds AI hardware/software for the semiconductor industry. We are seeking a Compiler Engineer focused on frontend and model bring-up to extend our IR, ops, and lowering passes for new LLM architectures.

You will validate bring-up against simulators and FPGA prototypes, ensuring models run correctly ahead of silicon arrivals, and collaborate with backend teams to map models onto our hardware.

Qualifications

• Hands-on ML compiler experience and shipping compiler passes.
• Deep familiarity with PyTorch internals and compilation stack (TorchDynamo, FX, torch.export).
• Knowledge of modern LLM architectures: attention, MoE routing, quantization.

Responsibilities

• Model Bring-up: extend frontend/mid-level support for new LLM architectures in our compiler.
• IR and Lowering: extend IR/ops/lowering passes with test coverage.
• Quantization and Numerics: represent new quantization schemes for accuracy.
• Architecture Tracking: monitor LLM landscape to plan next bring-ups.
• Cross-Team Handoffs: coordinate with backend compiler engineers and LLM serving.

Skills

ML compiler

PyTorch internals

LLM architectures

Quantization schemes

Attention mechanisms

Rust/C++ familiarity

Tools

TorchDynamo

FX

torch.export

Job description

Normal Computing | Build with Us Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), London, Copenhagen, and Seoul.

The Role

Normal's hardware runs the most memory-bound parts of large-model inference inside memory itself, rather than shuttling weights to a processor. The software that makes this programmable doesn't exist yet in any standard form. Our compiler is how models reach that hardware, and new LLM architectures show up faster than most compilers can track. A new attention variant, MoE routing scheme, or quantization approach has to be represented faithfully in our compiler before it can run, let alone run fast. As a Compiler Engineer focused on frontend and model bring-up, you will extend our IR, ops, and lowering passes so new LLM architectures are supported before a customer asks for them. This is a research-adjacent seat: you are expected to follow the literature closely, and publish on behalf of Normal. Our chip is pre-silicon, so you will validate bring-up against our simulators and FPGA prototypes first, so that models are ready to run when silicon arrives. What You Will Own Model Bring-up : Bring up frontend and mid-level support for new LLM architectures in our compiler, from attention variants to MoE routing schemes. Success means new architectures run correctly on our stack soon after release, ahead of customer demand. 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. What Makes You a Great Fit 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 Bonus Points 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, pip