Engineering Manager, AI Data Generation

Minimum qualifications:

• Bachelor’s degree, or equivalent practical experience.
  
• 8 years of experience in software development.
  
• 3 years of experience with full stack development, across back-end such as Java, Python, Golang, or C++ codebases, and front-end including JavaScript or TypeScript, HTML, CSS or equivalent.
• 3 years of experience in a technical leadership role overseeing projects.
• 2 years of experience in a people management, supervision/team leadership role.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
  
• 8 years of experience in software development.
• 5 years of experience building and developing large-scale infrastructure.
• 3 years of experience working in a structured organization.
• Experience in distributed systems or networks, or with compute technologies, storage, or hardware architecture.

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.

AI Data Generation Team (AI DGiT)'s charter is to generate generative AI data and evals for Google. Our team has been instrumental in performing LLM/GenAI model evaluations and model fine-tuning, e.g., using reinforcement learning from human feedback (RLHF) techniques. CrowdCompute is a large-scale general purpose platform that collects upwards of five million human answers a day on behalf of teams across Alphabet. DataGen is our evolving self-service data generation platform that enables a typical AI product developer to produce verifiable high-quality data-sets through automatable workflows with integrations into pre-training, post-training and evaluations infrastructure.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

The US base salary range for this full-time position is $197,000-$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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

• Set and communicate team priorities that support the broader organization's goals. Align strategy, processes, and selection-making across teams.
• Learn novel GenAI use cases in depth for their data needs and identify solutions leveraging the AI Data Platform.
• Develop the mid-term technical goals and roadmap within the scope of your team. Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
• Deliver and launch tools/solutions to collect, verify and deliver data/evals for a variety of GenAI use cases, including evaluation and improvement of generative AI models and launches.
• Review code developed by other engineers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).