Lead Compiler Engineer

Position Overview

We are seeking a talented ML Compiler Engineer to join our engineering team and lead the development of a compiler for a novel LLM accelerator architecture. This role focuses on building software systems that bridge high-level AI workloads with custom hybrid optical-electronic compute hardware, enabling breakthrough performance.

Key Responsibilities

• Design and implement toolchains for a custom LLM accelerator architecture

• Develop optimization strategies that map software algorithms efficiently to hardware implementations

• Build custom compiler components, including IR dialects, graph transformations, and lowering passes

• Optimize computational graphs and memory access patterns for specialized hardware

• Integrate with existing ML frameworks (e.g., PyTorch, JAX, Triton)

• Develop and maintain testing infrastructure to ensure compiler correctness and performance

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, or a related field

• 10+ years of industry experience

• 5+ years of experience in systems programming or compiler development

• Expert-level proficiency in Python and C

• Experience working with hardware compilers

• Familiarity with large language model architectures and their computational requirements

• Hands-on experience with compiler frameworks and optimization techniques

• Strong understanding of computer architecture, memory hierarchies, and parallel computing

• Experience with AI/ML accelerators (GPUs, TPUs, FPGAs) and their programming models

Preferred Skills

• Master’s degree in Computer Science, Computer Engineering, or a related field

• Strong background in graph theory and compiler-based graph transformations (MLIR experience is a plus)

• Experience working with abstract syntax trees (parsing, analysis, transformation)

• Experience debugging and instrumenting parallel systems

• Familiarity with structured, human-supervised AI or agentic coding workflows

• Experience with LLM quantization and model optimization techniques

• Background in high-performance computing and low-latency system design

• Familiarity with deep learning frameworks and neural network optimization

Technical Skills

• Programming Languages: Python, C (essential), Assembly

• Compiler Frameworks: LLVM, MLIR, GCC, custom backend development

• Graph Theory: Graph algorithms, DAG optimization, rewriting systems

• AST Processing: Parsing, analysis, and transformation

• Testing & QA: pytest, GoogleTest, static analysis tools

• CI/CD: Jenkins, GitHub Actions, GitLab CI

• LLM Technologies: Transformer architectures, attention mechanisms, quantization

• Development Tools: CMake, Git, Docker

• Parallel Tools: Profilers, debuggers, instrumentation tools

Technical Environment

• Languages: Python and C (primary), Assembly for low-level optimization

• Compiler Tools: LLVM, MLIR, GCC, custom compiler backends

• Testing: Automated test suites and continuous integration pipelines

• Frameworks: PyTorch, JAX, Triton, and custom inference engines

• Focus Areas: Compiler backend development, optimization passes, and hardware-software co-design