**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 be designing, coding, profiling, and iterating 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.

📌 C++ Engineer Ai Runtime (Monterrey, Mexico)
🏢 Baasi
📍 Monterrey