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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 So Cs, 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 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
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 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 :
- Bachelor’s 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 Tensor Flow, Py Torch, 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.g., 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
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.
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About Silicon Labs
Silicon Labs
PublicDesigns and manufactures semiconductors, other silicon devices and software, which it sells to electronics design engineers and manufacturers in In...
1,001-5,000
Employees
Austin
Headquarters
Reviews
4.2
1 reviews
Work Life Balance
3.5
Compensation
3.0
Culture
4.0
Career
3.5
Management
4.0
75%
Recommend to a Friend
Pros
Fast-moving hiring process
Supportive interview environment
Reasonable technical questions
Cons
Nerve-wracking senior engineer observation
Substantial coding problems
Long technical interview duration
Salary Ranges
0 data points
Intern
Intern · Embedded SWE Intern
0 reports
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total / year
Base
-
Stock
-
Bonus
-
Interview Experience
1 interviews
Difficulty
3.0
/ 5
Duration
14-28 weeks
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Final Round Interview
6
Offer
Common Questions
Technical Knowledge
Coding/Algorithm
Behavioral/STAR
Past Experience
Culture Fit
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