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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal AI Compiler Engineer - **Company:** Renesas Electronics America Inc. - **Location:** Austin, TX, United States (Remote available) - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Artificial Neural Networks, C++ (Programming Language), Microprocessors, Python (Programming Language), Performance Tuning, Scrum Methodology, Tensorflow, Pytorch, Safety Critical Systems, Generative AI, Information Technology, Low Latency, ONNX (Open Neural Network Exchange) Format, Data Analytics - **Published:** May 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=1e7e1a0f0290be58 ## About the Role * MS/PhD (or equivalent experience) in Computer Science, EE, or related field * Deep experience building AI compilers, accelerator backends, or graph optimization frameworks. * Strong expertise in graph optimization and performance optimization for NPUs or custom accelerators * Experience with MLIR, LLVM, TVM-like systems, or proprietary compiler IRs. * Excellent C/C++ and Python skills * Solid understanding of AI inference workloads (CNNs, transformers, perception or generative models) * Strong communication skills are required, e.g. agile development experience in Scrum team (Product Owner or Scrum Master) Nice to Have: * Experience with automotive or safety-critical systems. * Background in heterogeneous SoCs (CPU/GPU/DSP/NPU) * Performance modeling or hardware-software co-design experienc ## Description Renesas Electronics is searching for a hands-on AI Compiler Engineer who thrives at the convergence of cutting-edge AI, compiler tech, and hardware design. Here, you'll not only architect and scale a production-class AI compiler toolchain, but also rethink how AI automates, optimizes, and accelerates every step of building and deploying neural networks on Renesas SoCs. You'll work shoulder-to-shoulder with visionary engineers-both human and AI-enabling adaptive compilers that learn, evolve, and redefine what's possible for embedded intelligence. With a relentless focus on hardware-software co-design, you'll collaborate across teams to translate high-level AI models into blazing-fast, energy-efficient executables, unlocking the full potential of our silicon for real-world impact. Innovation here isn't a catchphrase-it's your everyday. What You'll Do * Own the design, implementation, and evolution of an AI compiler toolchain that leverages AI agents to seamlessly map neural networks onto Renesas SoC platforms. * Pioneer new graph transformations, lowering, scheduling, and codegen strategies for CPUs and custom accelerators, driven by insights from AI-powered analytics. * Build deep integrations with leading AI frameworks (PyTorch, TensorFlow, ONNX, and more), using AI agents to rapidly onboard new model architectures and ops. * Push the envelope on quantization, operator fusion, memory planning, and layout transformations-combining human expertise and AI-guided design for state-of-the-art results. * Partner with hardware and software architects, kernel hackers, and AI agents to co-design next-gen compiler and accelerator features, aligning silicon and code for maximum impact. * Diagnose and crush performance bottlenecks with AI-enabled profiling and diagnostics, relentlessly tuning for latency, throughput, and power efficiency. * Level up validation, benchmarking, and regression pipelines by harnessing AI agents-ensuring compiler correctness and world-class performance, release after release. * Uplevel the developer experience by streamlining usability, diagnostics, and documentation-AI agents are your copilots for user support, troubleshooting, and rapid iteration. ## Related Videos - [Developing an AI.SDK](https://www.wearedevelopers.com/videos/198-developing-an-ai-sdk) - [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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