NXP Semiconductors ↗

2026 Campus - Embedded Machine Learning Engineer

📍 Beijing, China 🇨🇳

full-time

entry-level

Expired

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TensorFlowPyTorchCC++Python

SemiconductorConsumer Electronics

Responsibilities

Develop and optimize Embedded ML inference engines for microcontrollers.
Train and fine-tune machine learning models using TensorFlow and PyTorch to be deployed on resource-constrained devices.
Implement and experiment with techniques to improve model performance on low-power and memory-limited devices.
Collaborate with cross-functional teams to integrate ML solutions into embedded systems.
Conduct research on new machine learning techniques and tools specifically for Embedded ML applications.
Optimize machine learning algorithms to meet the performance and resource constraints of embedded systems.
Stay up-to-date with the latest advancements in Embedded ML by reading and interpreting technical articles and research papers.

Requirements

Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.
Strong experience with TensorFlow and PyTorch for model training and deployment.
Proficiency in programming languages such as C, C++, and Python.
Extensive experience in embedded software development and machine learning.
Excellent programming skills in at least one of the following: C, C++, or Python.
Proven ability to read and understand technical articles and research papers in English.
Strong problem-solving skills and attention to detail.
Good communication skills and the ability to work collaboratively in a team environment.
Preferred Qualifications:
Proven experience with deploying machine learning models to embedded devices, specifically for Embedded ML applications.
Familiarity with embedded systems, microcontrollers, and real-time operating systems (RTOS).
Deep understanding of software development life cycle and best practices for embedded systems.
Previous experience in a full-time role or significant project in a related field.
Expertise in optimization techniques for low-power and low-latency machine learning models.