Compiler Code Gen Engineer

Lemurian Labs•Toronto, ON

139d

About The Position

We're looking for a Compiler Code Generation Engineer to design and build the core code generation capabilities inside our high-performance, portable AI compiler. This is a foundational engineering role where you'll work directly on the subsystems that translate high-level ML computations into optimized machine code across heterogeneous hardware targets. If you love digging into instruction selection, register allocation, and low-level target-specific optimization and you want that work to matter at the scale of the next era of AI infrastructure, this role is for you.

Requirements

BS degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience.
4+ years of experience working with compilers.
Deep knowledge of compiler algorithms and data structures.
Experience with and genuine interest in low-level code generation, object file manipulation, and target-specific optimizations.
4+ years of experience with C/C++.
Strong written and verbal communication skills; ability to write clear and concise technical documentation.

Nice To Haves

Master's or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.
Knowledge of traditional compiler techniques: instruction selection, register allocation, dominance analysis, def-use chains.
Familiarity with calling conventions, APIs, linking, and relocations.
Working knowledge of LLVM.
Experience with loop optimizations, vectorization, unrolling, fusion, and parallelization.
Experience with machine learning workloads and their hardware demands.

Responsibilities

Design, develop, maintain, and improve our heterogeneous AI compiler.
Design and implement new code generation capabilities based on our novel compiler architecture.
Propose improvements and extensions to our compiler architecture in response to advances in ML model design and hardware.
Apply the latest techniques in parallelization and partitioning to automate kernel generation and exploit highly optimized execution paths.
Use performance data to identify optimization opportunities and drive measurable improvements.
Collaborate with our product team to understand the evolving needs of ML engineers and translate those needs into architectural improvements.