Posted on: 04/12/2025
The Role :
As a Senior QA Engineer in the AI/ML team at Silicon Labs, you will play a pivotal role in defining and upholding quality standards for machine learning and deep learning models deployed on IoT edge devices.
Based at our Hyderabad Software Centre of Excellence, you will design automated test frameworks, validate model performance under real-world conditions, and ensure seamless integration of AI technologies into next-generation IoT products.
Meet the Team :
Youll be part of Silicon Labs newly established AI/ML SQA team, working at the forefront of innovation to deliver intelligent IoT solutions.
The team collaborates closely with ML developers, DevOps, and product engineers across geographies to support the development, testing, and deployment of ML models and data pipelines.
This team is responsible for building the foundation of quality assurance for ML models, enabling cutting-edge IoT products powered by artificial intelligence.
Responsibilities :
- Develop and execute test strategies for machine learning, deep learning, and Tiny LLM models running on IoT edge devices.
- Validate model accuracy, robustness, and scalability under real-world IoT data conditions.
- Design automated frameworks to test data pipelines, feature extraction, inference performance, and edge/cloud integration.
- Ensure seamless integration of ML/DL modules into the IoT platform software stack (firmware, middleware, connectivity, and cloud APIs).
- Collaborate with ML developers to ensure models meet production-grade quality standards.
- Work with DevOps engineers to integrate ML model validation into CI/CD workflows.
Requirements :
- Bachelors degree in Electrical Engineering or Computer Science (or equivalent combination of education and experience.
- 5+ Years of relevant industry experience.
- Strong understanding of machine learning frameworks such as TensorFlow, PyTorch, scikit-learn.
- Experience in designing and executing automated test frameworks for ML/DL systems.
- Familiarity with DevOps tools like Docker, Kubernetes, Jenkins, GitLab CI.
- Exposure to ML model optimization for edge devices (e., TensorRT, OpenVINO, Edge TPU).
- Knowledge of MLOps practices, including model versioning and deployment workflows.
- Understanding of natural language processing and Tiny LLMs is a plus.
Benefits & Perks :
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Posted By
Posted in
Quality Assurance
Functional Area
QA & Testing
Job Code
1584387
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