8 publicaciones de empleo destacadas de quality engineer (electronics) en guadalajara, jal (¡Están contratando!)

Senior AI Software Engineer
NXP Semiconductors —Guadalajara, Jal.
- Tiempo completo
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Servicios Comerciales Amazon Mexico S. de R.L. de C.V. —Zapopan, Jal.
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Senior AI Software Engineer

NXP Semiconductors -
Guadalajara, Jal.

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Información del empleo

Tiempo completo

Cualificaciones

TensorFlow
Derecho
PyTorch
Inglés
C++
Licenciatura
Hardware de computadora
NLP
Linux
Jenkins
Python

Descripción completa del empleo

Job Title: Senior 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 5 + 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.

Preferred Qualifications

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.

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