C++ Engineer Ai Runtime (Santiago, Chile) (Rancagua)

Join to apply for the C++ Engineer AI Runtime (Santiago, Chile) role at Baasi About Us We are a stealth-mode startup building next-generation infrastructure for the AI industry. Our team has decades of experience in software, systems, and deep tech. We are working on a new kind of AI runtime that pushes the boundaries of performance and flexibility, making advanced models portable, efficient, and customizable for real-world deployment. If you want to be part of a small, fast-moving team shaping the future of applied AI systems, this is your opportunity. Role We are looking for a C++ Engineer with strong systems and GPU programming background to help extend and optimize an open-source AI inference runtime. You will work on low-level internals of large language model serving, focusing on : Dynamic adapter integration (e.g., LoRA / QLoRA) Incremental model update mechanisms Multi-session inference caching and scheduling GPU performance improvements (Tensor Cores, CUDA / ROCm) This is a hands-on role : you will design, code, profile, and iterate on high-performance inference code that runs directly on CPUs and GPUs. Responsibilities Implement support for runtime adapter loading (LoRA), enabling models to be customized on the fly without retraining or model merges. Design and implement mechanisms for incremental model deltas, allowing models to be extended and updated efficiently. Extend runtime to handle multi-session execution, with isolation and caching strategies for concurrent users. Optimize core math kernels and memory layouts to improve inference performance on CPU and GPU backends. Collaborate with backend and infrastructure engineers to integrate your work into APIs and orchestration layers. Write benchmarks, unit tests, and profiling tools to ensure correctness and measure performance gains. Contribute to system architecture discussions and help define the roadmap for future runtime features. Requirements Strong proficiency in modern C++ (C++14 / 17 / 20) and systems programming. Solid understanding of low-level performance optimization : memory management, multithreading, SIMD, cache efficiency. Experience with CUDA and / or ROCm / HIP GPU programming. Familiarity with linear algebra kernels (matrix multiply, attention) and how they map to hardware acceleration (Tensor Cores, BLAS libraries, etc.). Exposure to machine learning inference frameworks (e.g., llama.cpp, TensorRT, ONNX Runtime, TVM, PyTorch internals) is a plus. Comfortable working in a Unix / Linux environment; experience with build systems (CMake, Bazel) and CI pipelines. Strong problem-solving and debugging skills; ability to dive deep into both code and performance traces. Self-motivated and able to thrive in a fast-moving startup environment. Nice to Have Experience implementing LoRA or adapter-based fine-tuning in inference runtimes. Knowledge of quantization methods and deploying quantized models efficiently. Background in distributed systems or multi-GPU orchestration. Contributions to open-source ML / AI systems. Why Join Build core IP at the intersection of AI and systems engineering. Work with a highly technical founding team on problems that are both intellectually challenging and commercially impactful. Opportunity to shape the direction of a new AI platform from the ground up. Competitive compensation (contract or full-time), equity potential, and versátil remote work. Apply Please use this link to apply : #J-

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