About the Company
Google is a global technology leader, dedicated to improving the lives of billions through its innovative products and services. Our Smart Home division, including Nest products, is at the forefront of creating intuitive, connected devices that enhance daily living. We leverage cutting-edge AI and IoT technologies to build intelligent environments that anticipate user needs and deliver seamless experiences. Join a team where your work will have a tangible impact on the future of home automation.
Job Description
We are seeking a talented and detail-oriented Smart Home Sound Classifier with an Internet of Things (IoT) focus to join our innovative Smart Home division. In this role, you will be instrumental in developing and refining the audio recognition capabilities of our next-generation smart home devices. You will work with diverse audio datasets to train and optimize machine learning models that can accurately classify sounds within a home environment, from voice commands to alarms, animal sounds, and environmental noise. This is a critical position that directly contributes to the intelligence and responsiveness of our IoT ecosystem, enhancing user experience and device functionality. You will collaborate closely with hardware engineers, software developers, and UX designers to bring sophisticated audio intelligence to life.
Key Responsibilities
- Design, collect, and annotate audio datasets relevant to smart home environments for machine learning model training.
- Develop and implement machine learning models for sound classification, anomaly detection, and acoustic event recognition on embedded IoT devices.
- Evaluate model performance, identify areas for improvement, and iterate on model architectures and features.
- Work with real-world audio data to ensure robustness and accuracy across diverse acoustic conditions.
- Collaborate with hardware teams to optimize audio input pipelines and sensor integration for sound classification.
- Partner with software engineers to integrate trained models into device firmware and cloud services.
- Contribute to the research and development of novel audio processing techniques and machine learning algorithms.
- Document methodologies, experimental results, and provide technical reports to cross-functional teams.
Required Skills
- Proficiency in Python and relevant machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Strong understanding of digital signal processing (DSP) and audio feature extraction techniques.
- Experience with machine learning model development, training, and evaluation for audio data.
- Familiarity with IoT device architectures and constraints for embedded ML.
- Solid analytical and problem-solving skills, with a focus on data-driven decision making.
- Excellent communication and teamwork abilities.
Preferred Qualifications
- Master's or Ph.D. in Computer Science, Electrical Engineering, Acoustics, or a related field.
- Experience with cloud platforms for ML model deployment and management (e.g., Google Cloud Platform, AWS, Azure).
- Familiarity with various audio codecs and formats.
- Knowledge of real-time operating systems (RTOS) or embedded Linux.
- Experience with sound event detection (SED) or speech recognition systems.
- Contributions to open-source projects or publications in relevant fields.
Perks & Benefits
- Competitive salary and equity package.
- Comprehensive health, dental, and vision insurance.
- Generous paid time off and flexible working arrangements.
- Opportunities for professional development and continuous learning.
- Access to state-of-the-art facilities and equipment.
- Free meals, snacks, and beverages at the office.
- On-site fitness centers and wellness programs.
- Employee assistance program and mental health support.
How to Apply
If you are interested in this position, please click the "Apply Now" button below. To ensure your application is properly considered, please prepare the following:
- An up-to-date Resume or CV
- A brief cover letter summarizing your experience and motivation
Applications are reviewed on a rolling basis. Only shortlisted candidates will be contacted for an interview.
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