Staff Deep Learning Compiler Engineer

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Skills you'll use

What you'll do

• Deep Learning Compiler Infrastructure & Optimization
• Design, implement, and maintain compiler optimization passes targeting Quadric’s processor architecture using frameworks like MLIR, Apache TVM, or LLVM.
• Develop lowering pathways from high-level machine learning frameworks (PyTorch, TensorFlow, ONNX) down to optimized low-level kernel code.
• Optimize neural network performance for memory throughput, latency, compute unit utilization, and power consumption.
• Neural Network Model Parsing & Graph Transformation
• Implement graph-level optimizations, including operator fusion, layout transformation, quantization (INT8/FP16), and memory allocation strategies.
• Analyze novel deep learning model topologies (Transformers, CNNs, Vision-Language Models) and extend compiler support for new operators and primitives.
• Benchmark and profile end-to-end model performance to identify and resolve compiler bottlenecks.
• Hardware-Software Co-Design
• Collaborate closely with hardware and micro-architecture teams to define instruction set extensions, hardware acceleration features, and compiler requirements.

Who can apply

• Participate in hardware bring-up and validation efforts on FPGA and ASIC platforms.
• Successfully integrate support for key deep learning models (e.g., modern Transformer architectures or Vision models) into Quadric's compiler pipeline.
• Implement custom graph and codegen optimization passes that deliver measurable performance improvements on target benchmarks.
• Partner with the architecture team to influence the next-generation micro-architecture definition through data-driven workload analysis.
• 8 years+ experience alongwith BS, MS, or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
• Hands-on experience developing deep learning compilers or compiler infrastructures (e.g., MLIR, Apache TVM, LLVM, XLA, TensorRT, or Glow).
• Strong proficiency in C++ (14/17/20) and Python, with solid fundamentals in data structures, algorithms, and object-oriented design.
• Familiarity with modern AI/ML frameworks (PyTorch, TensorFlow, ONNX) and deep learning operator representations.

About Quadric

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