Research Engineering Manager, Multimodal AI, Google Labs

Minimum qualifications:

• Bachelor’s degree in Computer Science, Electrical Engineering, a related technical field, or equivalent practical experience.

• 8 years of experience with software development or machine learning research and engineering in Python or C++, including designing or deploying machine learning systems.

• 3 years of experience in a technical leadership or people management role leading engineers or research scientists in AI/ML, computer vision, audio, or multimodal systems.

Preferred qualifications:

• Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or Audio Processing.

• Experience as a technical lead manager guiding multidisciplinary teams of research scientists and software engineers from early research framing through production deployment.

• Experience building generative video, speech and audio synthesis or translation, multimodal perception models, or model and evaluation pipelines.

• Experience optimizing low-latency audio-video streaming, real-time multimodal model serving, or interactive computer vision systems.

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.

As a Research Engineering Manager and Technical Lead Manager in Google Labs, you will lead a focused, multidisciplinary pod of research scientists and software engineers. You will drive the technical strategy across large-scale audio-visual data curation, model training, rigorous model evaluation, and real-time streaming inference to support emerging multimodal research prototypes.

Labs is a group focused on incubating early-stage efforts in support of Google’s mission to organize the world’s information and make it universally accessible and useful. Our team exists to help discover and create new ways to advance our core products through exploration and the application of new technologies. We work to build new solutions that have the potential to transform how users interact with Google. Our goal is to drive innovation by developing new Google products and capabilities that deliver significant impact over longer timeframes.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google. Responsibilities:

• Guide and mentor a focused, multidisciplinary pod of research scientists and software engineers as a technical lead manager building real-time generative audio, speech, and visual AI systems.

• Define and execute the technical roadmap for high-signal audio-visual data curation, multimodal training, and end-to-end model evaluation frameworks in partnership with other AI teams.

• Contribute to low-latency multimodal serving and streaming inference systems that leverage audio and visual models in real-time user experiences.

• Partner with product and engineering leaders across DeepMind, Google Beam, and consumer product surfaces to transition applied research breakthroughs into production.

• Establish engineering and scientific excellence across model iteration, real-time streaming performance, and cross-Google platform integration.