Our Client Currently looking for AI Compiler Engineer

You will work closely with software, hardware, and AI engineers to enable efficient inference execution of best-in-class GenAI LLMs, optimizing code generation and advance compiler infrastructure.
Your work will directly influence performance and scalability across AI inference stack.

Your main responsibilities will include:

• Leading and contributing to:
• Design, implement, and maintain compiler components for AI inference workloads, focusing on graph lowering, optimization, and code generation (from frameworks like PyTorch, TensorFlow, or ONNX to hardware).
• Enable validation on prototype silicon or simulators or real hardware
• Profile, debug and optimize model execution to maximize throughput and minimize latency together with energy efficiency.
• Contribute to open-source compiler ecosystems (e.g., LLVM, MLIR, XLA, IREE)
• Collaborate with hardware architecture team to leverage hardware features, maximizing performance.

WHAT WE ARE LOOKING FOR:
Technical skills:

• MLIR / LLVM / IREE / XLA compiler development
• Model formats: ONNX, TensorFlow, PyTorch, HuggingFace
• Low-level optimization (SIMD, vectorization, scheduling, data-tiling)
• Familiarity with hardware accelerators (GPU, NPU, TPU, CPU)
• Software profiling and performance analysis tools
• Git, CI/CD workflows

Nice to have:

• Experience with RISC-V architecture or AI accelerators
• Contributions to open-source MLIR or compiler projects
• Knowledge of quantization, mixed precision, and graph optimization techniques
• Exposure to system software, runtime APIs, or driver-level integration
• Background in heterogeneous computing and parallel processing

• Are you interested in this position?

   

  Apply by clicking on the “Apply Now” button below!

   

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