Compiler Optimization Engineer (AI)

Location: Santa Clara, California, or Toronto, Canada

Company

hirify.global is building a hardware-agnostic software stack that enables AI workloads to run efficiently across different chips, clouds, and system scales.

What you will do

• Design, develop, and maintain the graph optimization layer of a heterogeneous AI compiler.
• Implement graph transformations such as operator fusion, layout propagation, dead code elimination, constant folding, and algebraic simplification.
• Define and evolve the intermediate representation to support optimization of advancing ML model architectures.
• Analyze performance data and deliver measurable improvements in model throughput and latency.
• Collaborate with compiler front-end and code-generation teams to maintain clear IR interfaces and optimization pipelines.
• Develop testing and validation infrastructure and prototype optimization strategies for new models and hardware targets.

Requirements

• Bachelor’s degree in Computer Science, Computer Engineering, or equivalent practical experience.
• 4+ years of compiler experience focused on intermediate representation design or optimization passes.
• Deep knowledge of graph-level optimization techniques, including fusion, tiling, and layout transformations.
• 4+ years of experience with C/C++.
• Strong written and verbal communication skills, including the ability to create concise technical documentation.

Nice to have

• Master’s or PhD in Computer Science or Computer Engineering.
• Experience with polyhedral models, affine analysis, MLIR, XLA, or similar graph-level IR frameworks.
• Knowledge of hardware memory hierarchies, GPU or accelerator performance, and ML framework internals such as PyTorch, JAX/XLA, or TensorRT.
• Understanding of ML architectures, quantization, sparsity, or other model-level optimization techniques.
• Contributions to open-source compiler or ML infrastructure projects.

Culture & Benefits

• Ownership of a critical compiler layer with direct, measurable impact on model performance.
• Work on graph-level AI infrastructure problems across diverse hardware targets and model architectures.
• Competitive compensation with equity and potential company bonus opportunities.
• Medical, dental, and vision coverage, retirement savings, and supplemental wellness benefits.