Senior Software Engineer

Segment (Twilio)
Albacete, Spain
8 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience required
5 years minimum
Working hours
Regular working hours
Languages
English

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Computer Vision Automation of Tests C++ (Programming Language) Profiling Continuous Integration Github Python (Programming Language) Linux System Administration Machine Learning Performance Tuning
+15 more
Tensorflow Pytorch Delivery Pipeline Large Language Models Caffe Deep Learning Generative AI Low Latency ONNX (Open Neural Network Exchange) Format HuggingFace Build Tools Machine Learning Operations Software Performance Automation Anywhere Jenkins

Job description

Job Title: Senior AI Software EngineerCompany: 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 SummaryAs 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 ResponsibilitiesCollaborate 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 ResponsibilitiesContribute 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 QualificationsBachelor’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 QualificationsExperience 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 GainHands?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-FCC3More information about NXP in Mexico…#LI-fcc3#J-*****-Ljbffr

Requirements

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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