Software Engineering Manager, AI/ML, Google Home Conversational Experiences

Socket.dev; Mountain View (CA); On-site; USD 207,000 - 300,000; Full time

Job summary

Google is seeking an experienced Software Engineering Manager to lead the Gemini for Home conversational experiences team. You will shape architecture, manage multiple engineering teams across locations, and deliver large-scale, cross-functional AI projects with emphasis on performance, safety and reliability.

You will guide product strategy, partner with researchers and data scientists, and help grow engineers while driving a high-performance culture in a fast-paced environment.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience with one or more ML areas: speech/audio, RL, ML infrastructure, or related field.
• 3 years in a technical leadership role.
• 2 years in a people management or team leadership role.

Responsibilities

• Drive the technical architecture and roadmap for the conversational experiences platform powering Gemini for Home across task-oriented and open-domain dialogue systems.
• Architect and deploy hybrid AI systems combining generative LLMs with deterministic components to maximize conversational quality, safety, and operational reliability.
• Diagnose production quality gaps, reduce end-to-end latency, and resolve complex issues across context ingestion, multimodal inputs, memory, and dialogue execution pipelines.
• Partner cross-functionally with research scientists, product managers, and data scientists to evaluate algorithms, design scalable data infrastructure, and launch user-facing features.
• Lead and grow an engineering team, setting clear technical direction, guiding individual career development, and fostering a high-performance culture.

Skills

Software development

ML design

ML infrastructure

Speech/Audio/LLMs

Technical leadership

People management

Education

Bachelor's degree / equivalent practical experience

Master's degree / PhD (preferred)

Job description

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 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.
• 3 years of experience in a technical leadership role.
• 2 years of experience in a people management or team leadership role.

Preferred qualifications:

• Master's degree or PhD in Engineering, Computer Science, or a related technical field.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Applied research expertise with LLMs, preferably for NLP purposes.
• Ability to effectively operate with flexibility in a fast paced, constantly evolving team environment.
• Excellent written and verbal communication skills in English.

About the job:

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering 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 Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. 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.

With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.

We are the Conversational Experiences team that powers the Gemini-based dialog engine for Gemini for Home, bringing improvements and new capabilities to how users can talk to and get things done with Google Home devices and app.

In this role, you will maintain and improve the conversational platform powering Gemini for Home.

You will combine generative AI solutions with classic deterministic architectures to raise conversational quality and drive down end-to-end latency.

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: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Drive the technical architecture and roadmap for the conversational experiences platform powering Gemini for Home across task-oriented and open-domain dialogue systems.
• Architect and deploy hybrid AI systems combining generative large language models with deterministic components to maximize conversational quality, safety, and operational reliability.
• Diagnose production quality gaps, reduce end-to-end system latency, and resolve complex issues across context ingestion, multimodal inputs, memory, and dialogue execution pipelines.
• Partner cross-functionally with research scientists, product managers, and data scientists to evaluate algorithms, design scalable data infrastructure, and launch user-facing features.
• Lead and grow an engineering team, setting clear technical direction, guiding individual career development, and fostering a high-performance culture.