AI Software Engineer

Requirements

• Experience with NPU architectures , compiler toolchains, and runtime environments.
• Exposure to a variety of AI/ML/DL frameworks such as TensorFlow, PyTorch, Caffe , and ONNX pipelines.
• Strong skills in developing AI workflows in both C++ and Python .
• Experience in hardware/software performance evaluation and profiling.
• Ability to work effectively both independently and in collaborative team settings .
• Strong aptitude for learning new tools, technologies, and AI workflows quickly.
• What You Will Gain
• Hands ‑ on experience with AI accelerator hardware , NXP SDKs , and next ‑ generation AI solutions.
• Opportunity to work with a wide range of customer models across CV, LLM, VLM , and Generative AI domains.
• Deep exposure to end ‑ to ‑ end AI pipelines, benchmarking methodologies, and model ‑ zoo development.
• Learning environment focused on innovation, performance optimization, and practical deployment of AI workloads.
• #LI-FCC3
• More information about NXP in Mexico...

Benefits

Additional Information

Job Title: AI Software Engineer Company: NXP Semiconductors México, S. de R.L. de C.V. NXP offers an inclusive, collaborative, and innovation-driven work culture where engineers have the freedom to explore ideas, contribute to cutting-edge technologies, and grow their technical depth. Job Summary As part of ongoing enhancements to our Discrete NPU Model Zoo , we are looking for AI Engineers to contribute to the design, development, and optimization of model ‑ zoo infrastructure supporting next ‑ generation computer vision , generative AI , and LLM workloads . You will work on developing compilation, inference, and performance workflows for AI accelerators, while helping build end ‑ to ‑ end demos and benchmarking pipelines. CNN Model Zoo Responsibilities Collaborate on scaling up our CNN ‑ based model zoo. Add new models and their variants in both Python and C++. Improve accuracy across ~50 existing models. Re ‑ implement variant models in PyTorch for integration with the MCW tool. Use the MCW tool for optimization, pruning, and performance tuning. Automate model testing, validation, and optimization flows where feasible. Develop gstreamer / NXP nnstreamer plugins and end ‑ to ‑ end inference pipelines. Build high ‑ quality demos showcasing model capabilities on NPU accelerators. LLM/VLM Model Zoo Responsibilities Contribute to the development and expansion of the LLM model zoo. Use Hugging Face APIs to build required pipelines and workflows (Python and C++). Integrate and support multiple variants of LLMs and VLMs. Develop automated testing frameworks and Jenkins ‑ based CI pipelines. Execute benchmarking, performance profiling, and validation of LLM/VLM models. Build demos demonstrating large ‑ model capabilities on our NPU platforms. Job Qualifications Bachelor's degree in Electronics/Computer/Systems/Robotics Engineering or similar with 3+ years of experience in Machine Learning or related fields. Strong proficiency in Python (AI scripting & workload automation) and C/C++ (low ‑ level performance work). Solid understanding of deep learning architectures across CV, NLP, or generative models. Experience building demos/applications using CNN and LLM models. Ability to measure and report latency, throughput, accuracy, and power for AI workloads. Familiarity with LLM benchmarking frameworks such as MMLU, HumanEval, GSM8K, ARC Challenge, GPQA , etc. Experience working with Hugging Face APIs , model repositories, and deployment workflows. Knowledge of Linux environments , build systems, and optionally, driver ‑ level basics. Experience with GitHub , CI/CD pipelines, and automated testing frameworks. Fluency in English (written and spoken) is required.

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