Engineering Manager, AI Innovation and Research, AI/ML
2 weeks ago
- 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 building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains.
- 3 years of experience in a technical leadership role; overseeing projects. 2 years of experience in a people management, supervision/team leadership role.
- Master's degree or PhD in Computer Science or related technical field.
- 3 years of experience working in an matrixed organization
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.
Responsibilities
- Lead and mentor a team of AI/ML engineers, guiding them in the development, training, evaluation, and deployment of AI/ML models, ensuring integration into production systems.
- Collaborate with Platforms & Devices (P&D) product teams and research teams to define and deliver AI-based features, anticipating future needs.
- Foster relationships and facilitate collaboration with the Mountain View-based team, leveraging their proximity and partnerships with P&D product teams to improve team efficiency and overall impact.
- Identify common needs across multiple engagements, driving the development of shared infrastructure and foundational models that serve as a common tool set and blueprints for accelerated future engagements.
- Advocate knowledge transfer and cultivate AI expertise within the P&D product teams, enabling them to independently lead and execute future AI-driven initiatives.
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