Job Description

Minimum qualifications:

• Bachelor's degree in Computer Science, Communications, Electrical Engineering, a related technical field, or equivalent practical experience.
• 8 years of experience designing and deploying ML models.
• 3 years of experience in a technical leadership role.
• 2 years of experience in a people management or team leadership role.
• Experience in shipping ML solutions on resource-constrained hardware (Embedded, Mobile, or IoT), with a focus on optimizing for latency, power consumption, and memory footprint.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• 3 years of experience working in a matrixed organization.
• Experienced in Linux camera software stack development such as camera driver, hal, framework and application.
• Experience implementing CV algorithms and building automated evaluation pipelines and metrics to ensure model accuracy and reliability in real-world environments.
• Experience in IoT camera and smart home technologies development.
• Proven ability to lead complex ML projects from conceptual design to successful production, providing technical direction for on-device products or solutions.

About the job

Like Google's own ambitions, the work of a Software Engineer (SWE) goes way beyond just Search. SWE Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of engineers. You not only optimize your own code but make sure engineers are able to optimize theirs. As a SWE Manager you manage your project goals, contribute to product strategy and help develop your team. SWE teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.

The Google Home Camera Software team builds smart home cameras that leverage intelligent AI to ensure home security and safety. We combine the best of Google AI, software, and hardware to create helpful experiences for our users. Our team researches, designs, and develops new technologies to make user interactions with computing more seamless.

In this role, you will lead an engineering team responsible for developing the camera on-device machine learning models, framework, ML pipeline for next generation AI cameras. You will manage the full lifecycle of AI-powered features, ensuring that models not only perform at high accuracy but also meet stringent power, latency, and thermal requirements. You will act as the primary technical bridge between software, hardware, and product teams to deliver seamless AI experiences to global users.

The Google Home team focuses on hardware, software and services offerings for the home, ranging from thermostats to smart displays. The Home team researches, designs, and develops new technologies and hardware to make users’ homes more helpful. Our mission is the helpful home: to create a home that cares for the people inside it and the world around it.

In this role, you will lead an engineering team responsible for developing the camera on-device machine learning models, framework, ML pipeline for next generation AI cameras. You will manage the full lifecycle of AI-powered features, ensuring that models not only perform at high accuracy but also meet stringent power, latency, and thermal requirements. You will act as the primary technical bridge between software, hardware, and product teams to deliver seamless AI experiences to global users.

Responsibilities

• Drive the design and maintenance of end-to-end ML deployment pipelines. Lead the team through model evaluation, fine-tuning, data processing, and debugging.
• Manage the critical trade-offs between model performance and hardware constraints (ARM SoCs, DSPs, NPUs). Be responsible for ensuring that ML-powered features respect device power consumption and latency budgets.
• Maintain high engineering standards for C++ development in an embedded context, ensuring scalable, and maintainable firmware and software architectures.