Silicon Labs (NASDAQ: SLAB) is the leading innovator in low-power wireless connectivity, building embedded technology that connects devices and improves lives. Merging cutting-edge technology into the world’s most highly integrated SoCs, Silicon Labs provides device makers the solutions, support, and ecosystems needed to create advanced edge connectivity applications. Headquartered in Austin, Texas, Silicon Labs has operations in over 16 countries and is the trusted partner for innovative solutions in the smart home, industrial IoT, and smart cities markets. Learn more at www.silabs.com.
The Role
As a Lead QA Engineer in the AI/ML team at Silicon Labs, you will play a pivotal role in shaping the quality standards for machine learning and deep learning models deployed on IoT edge devices. Based in our Hyderabad Software Centre of Excellence, you’ll lead the design of 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
You’ll 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 developed 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 development teams to ensure models meet production-grade quality standards.
- Work with DevOps engineers to integrate ML model validation into CI/CD workflows.
- Drive design and deep-dive discussions with development teams to improve test coverage and model reliability.
- Guide and mentor junior team members, fostering technical growth and best practices within the team.
Requirements :
- Bachelor’s degree in Electrical Engineering or Computer Science (or equivalent combination of education and experience.
- 8+ Years of relevant industry experience
- Strong experience in software quality assurance, particularly in testing ML/DL models and systems.
- Hands-on knowledge of machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
- Familiarity with DevOps tools like Docker, Kubernetes, Jenkins, GitLab CI, etc., for automated builds, testing, and deployment.
- Experience with ML model optimization for edge devices (e.g., TensorRT, OpenVINO, Edge TPU).
- Understanding of machine learning, deep learning, and natural language processing.
- Exposure to MLOps practices such as model versioning and lifecycle management.
- Proven ability to lead technical discussions and mentor junior engineers.
- Strong collaboration skills to work effectively across global teams.
Benefits & Perks
At Silicon Labs, you’ll be part of a highly skilled team where every engineer makes a meaningful impact. We promote work-life balance and a welcoming, fun environment.
- Equity Rewards (RSUs)
- Employee Stock Purchase Plan (ESPP)
- Insurance plans with outpatient cover
- National Pension Scheme (NPS)
- Flexible work policy
- Childcare support
Silicon Labs is an equal opportunity employer and values the diversity of our employees. Employment decisions are made on the basis of qualifications and job-related criteria without regard to race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status, or any other characteristic protected by applicable law.
