Compiler Engineer

Hanzo AI; EE. UU.; A distancia; USD 150.000 - 230.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote-first work

Descripción de la vacante

Hanzo AI is seeking a Senior Compiler Engineer to accelerate neural-network inference across GPUs, TPUs, and accelerators. You will work on graph optimization, IR lowering, and code generation for high-performance runtimes.

You will optimize compiler passes, build memory-management for inference, and measure performance with micro-benchmarks, profiling throughput and cost to deliver efficient AI inference at scale.

Formación

• Strong C++ or Rust skills and deep understanding of compiler architecture.
• Familiarity with MLIR, LLVM, or ML compilation tools such as TVM, XLA, TensorRT, or TorchDynamo.
• Understanding of GPU architecture, including tensor cores, shared memory, and warp scheduling.
• Careful approach to performance measurement, micro-benchmarks, and regression testing.

Responsabilidades

• Develop compiler optimizations and graph transformation passes for language-model inference.
• Use compiler intermediate representations to generate efficient machine code and kernel calls.
• Improve memory handling and inference techniques such as key-value caching, speculative decoding, and paged attention.
• Benchmark and profile performance, tracking throughput and cost.

Conocimientos

C++/Rust

Compiler architecture

IRs/optimizations

GPU architecture

Performance benchmarking

Memory optimization

KV caching

Herramientas

MLIR

LLVM

TVM

XLA

TensorRT

TorchDynamo

Descripción del empleo

Role overview

Build compiler and runtime capabilities that make neural-network inference faster and more efficient. The work spans graph optimization, intermediate-representation lowering, and hardware-specific code generation for GPUs, TPUs, and newer accelerator types.

Responsibilities

• Develop compiler optimizations and graph transformation passes for language-model inference.
• Use compiler intermediate representations to generate efficient machine code and kernel calls.
• Improve memory handling and inference techniques such as key-value caching, speculative decoding, and paged attention.
• Benchmark and profile performance, tracking throughput and cost.

Requirements

• Strong C++ or Rust skills and a deep understanding of compiler architecture, including intermediate representations, optimization passes, and backends.
• Familiarity with MLIR, LLVM, or machine-learning compilation tools such as TVM, XLA, TensorRT, or TorchDynamo.
• Understanding of GPU architecture, including tensor cores, shared memory, and warp scheduling.
• Careful approach to performance measurement, micro-benchmarks, and regression testing.

Nice to have

• Experience writing CUDA or Metal kernels for attention or fused operations.
• Contributions to open-source compiler or runtime projects.

Benefits and work setup

• Remote-first work across distributed locations, with autonomy and opportunities to contribute publicly to open-source projects.