Senior Software Engineer, AI/ML, Home Context and Foundation Models

Senior Software Engineer, AI/ML, Home Context and Foundation Models

corporate_fareGoogleplaceMountain View, CA, USA

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in one or more programming languages.
• 3 years of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
• 3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
• Experience designing and operating distributed computing systems for large-scale AI/ML workloads.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• 1 year of experience in a technical leadership role.
• Experience partnering with research teams to transition novel models into scalable, low-latency production services.
• Experience taking Machine Learning and Generative AI features from prototype to production environments.
• Experience with Generative AI techniques, including foundation models, multi-modal modeling, and vision modeling.
• Experience developing accessible technologies.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

The Home Intelligence team is responsible for the smarts behind the Smart Home -- everything from Voice Assistant-enabled speakers and displays to Cameras, Thermostats, and ambient sensors. Gemini is transforming how your entire home can come alive with intelligent capabilities that bring together the best of Google Home to help in the moment and take care of you and your family.

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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.

Responsibilities

• Build scalable offline and online multi-modal data and serving pipelines integrating camera/video intelligence, HomeGraph device state, and telemetry to supply high-value signals to Home Context and HFM.
• Design, develop, and productionize Home Context capabilities (such as Home Pulse, household routine modeling, and anomaly detection) and integrate them with flagship Google Home features including Visual Home Brief and Camera Captions.
• Partner closely with Research and Infrastructure teams to co-develop, evaluate, and productionize the Home Foundation Model (HFM) for real-world Smart Home applications.
• Integrate contextual understanding with Identity and Presence signals so downstream Home experiences understand the user.
• Collaborate with Product, Research, and Vertical engineering teams to define target use cases, build evaluation benchmarks, and drive model and system quality improvements from prototype to launch.