Compiler Optimization Engineer

You will own the middle tier of an AI compiler stack, where high-level model graphs are transformed, simplified, and prepared for efficient code generation. You will design and implement optimization passes such as operator fusion, layout propagation, dead code elimination, and constant folding. You will define and evolve the intermediate representation, analyze performance data to close optimization gaps, and collaborate with front end and code generation teams on clean IR interfaces. You will prototype new optimization strategies and contribute to testing and validation infrastructure across model types and hardware targets.

Responsibilities

• Design, develop, and maintain the graph optimization layer of a heterogeneous AI compiler
• Implement and extend graph-level transformation passes including operator fusion, layout propagation, dead code elimination, constant folding, and algebraic simplification
• Define and evolve the intermediate representation (IR) to support new optimization opportunities
• Analyze performance data to identify optimization gaps and drive improvements in throughput and latency
• Collaborate with front end and code generation teams to ensure clean IR interfaces and well-structured optimization pipelines
• Propose and prototype new optimization strategies in response to advances in model design and hardware capabilities
• Contribute to testing and validation infrastructure to ensure optimization correctness across model types and hardware targets

Requirements

• BS degree in Computer Science, Computer Engineering, or equivalent practical experience
• 4+ years of experience working with compilers, with a focus on intermediate representation design or optimization passes
• Deep knowledge of graph-level compiler optimization techniques — fusion, tiling, layout transformations, and related methods
• 4+ years of experience with C/C++
• Strong written and verbal communication skills; ability to write clear and concise technical documentation
• Preferred: Master's or PhD in Computer Science, Computer Engineering, or equivalent
• Preferred: Experience with polyhedral models or affine analysis for loop and tensor optimization
• Preferred: Familiarity with hardware memory hierarchies and layout decisions on GPUs or accelerators
• Preferred: Experience with MLIR, XLA, or similar graph-level IR frameworks
• Preferred: Experience with ML framework internals — PyTorch eager/compile mode, JAX/XLA, or TensorRT
• Preferred: Strong understanding of ML model architectures and their computational patterns
• Preferred: Knowledge of quantization, sparsity, or other model-level optimization techniques
• Preferred: Contributions to open-source compiler or ML infrastructure projects

Benefits

• Equity
• Company bonus opportunities
• Medical coverage
• Dental coverage
• Vision coverage
• Retirement savings plan
• Supplemental wellness benefits