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Software Engineer — Speech Dialog

Job in Mountain View, Santa Clara County, California, 94039, USA
Listing for: Google
Full Time position
Listed on 2026-06-27
Job specializations:
  • Software Development
    Software Engineer, AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 189000 - 284000 USD Yearly USD 189000.00 284000.00 YEAR
Job Description & How to Apply Below
Position: Staff Software Engineer — Real-Time Speech Dialog

Minimum qualifications:

  • Bachelor's degree or equivalent practical experience.
  • 8 years of experience in software development, and with data structures/algorithms.
  • 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
  • 5 years of experience in the Machine Learning field.
  • 5 years of coding experience in one or more of the following languages: C, C++, Java, or Python.

Preferred qualifications:

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  • 3 years of experience in a technical leadership role leading project teams and setting technical direction.
  • 3 years of experience working in a complex, matrixed organization involving cross-functional or cross-business projects.

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.

We are building the next generation of conversational capabilities, powered by the Gemini LLM. Our mission is to empower multimodal conversational agents with groundbreaking speech and audio capabilities. By starting with LLMs that natively understand rich audio input, we aim to create agents that can orchestrate all aspects of a dialog. This includes knowing when to listen and wait, when to interrupt, reading the emotive style, and coordinating complex multimodal interactions that span audio, video, and text.

As part of the speech team, our mission is to create, scale, and product ionize these rich conversational capabilities to a wide range of Google products and experiences.

The US base salary range for this full-time position is $189,000-$284,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

  • Partner with teams to design, develop, and deploy novel multimodal conversational capabilities.
  • Drive data collection and synthesis pipelines that are key to enabling new audio-first Gemini LLM capabilities.
  • Work closely with other speech teams on voice synthesis, noise-robustness, voice-match, non-speech audio, barge-in detection, etc.
  • Leverage larger LLMs to synthesize data for novel interactions (e.g., via prompt-engineering and few-shot learning).
  • Optimize data, API, and interaction design for streaming bi-directional dialog and rapidly prototype and evaluate new technologies.
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