Staff ML Compiler Engineer, d-Matrix

• Bachelor's degree in computer science or a related field with 10+ years of relevant industry experience (or MS with 8+ years of experience or PhD with 3+ years of experience)
• Strong proficiency in modern C++ (C++14/17/20) and compiler development
• Experience with modern compiler infrastructures such as LLVM, MLIR, or equivalent frameworks
• Experience with machine learning frameworks (e.g., PyTorch, TensorFlow, ONNX)
• Solid understanding of graph-level optimizations and IR transformations in ML compilers
• Experience with model partitioning strategies such as GSPMD, Sharding, and Distributed Execution

Desirable

• The d-Matrix compiler team is looking for exceptional candidates to help develop the front-end of our machine learning compiler
• The successful candidate will work on designing, optimizing, and lowering high-level machine learning representations to intermediate representations suitable for further compilation
• High-level IR transformations (e.g. , graph optimization, operator fusion, canonicalization)
• Dialect and IR design for machine learning frameworks
• Lowering and transformation of ML models from frameworks such as PyTorch, TensorFlow, and ONNX to compiler IRs such as MLIR and LLVM
• Performance optimization for compute graphs, including operator specialization, fusion, and memory layout transformations
• Model partitioning techniques, including:
• Graph-based parallelism strategies (e.g. , pipelined model parallelism, tensor parallelism, data parallelism)
• Automatic partitioning of large models across multiple devices using techniques like GSPMD (Generalized SPMD Partitioning)
• Placement-aware optimizations to minimize communication overhead and improve execution efficiency on distributed hardware
• The successful candidate will join a team of experienced compiler engineers and work closely with ML framework developers, hardware architects, and performance engineers to ensure efficient model execution