Lead Group Product Manager, Vertex AI/ML Development
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 10 years of experience in product management or related technical role.
- 5 years of experience taking technical products from conception to launch.
- 5 years of experience in people management, with technical leadership.
- Experience with the domain area of Generative AI or Large Language Models.
- Experience in building machine learning and AI systems for the model development lifecycle: data preparation, training, tuning, evaluation, experiment tracking, etc., with experience in delivering products in the market.
Preferred qualifications:
- MBA or advanced technical degree.
- 7 years of experience working cross-functionally with engineering, UX/UI, sales finance, and other stakeholders.
- 5 years of experience in a role preparing and delivering technical presentations to executive leadership.
- 5 years of experience in software development or engineering.
About the job
The Vertex AI/ML Platform Team delivers the leading platform for the full AI/ML lifecycle, supporting Google, open source (like Llama 4), and third-party models (like Anthropic's Claude). Our ML Development team focuses on notebooks, training, tuning, evaluation, explainable AI, and pipelines. The ML Production team manages prediction, third-party model services, provisioned throughput, and Model Garden. We also manage global chip capacity for all Vertex and third-party model services.
As a Product Manager, you will lead the development of our ML development tools (notebooks, training, tuning, evaluation, etc.) for Google Cloud AI. Leading a team of product managers, you'll support internal teams and global external customers across all industries. You'll drive the product roadmap and commercial success of these products.
You'll also be a key spokesperson for Vertex AI's ML development tools internally and externally, engaging with Cloud leadership, leading customers, and industry analysts. Furthermore, you'll collaborate with infrastructure teams and internal product teams leveraging Vertex's tools.
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 $227,000-$320,000 + 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
- Refine/Communicate the goal and strategy for our AI products, ensuring alignment with broader objectives.
- Work closely with engineering, product and solutions teams, including Google DeepMind, to prioritize and deliver machine learning solutions. Collaborate cross-functionally to build partnerships and ensure our products benefit from their technologies.
- Collaborate with UXR and conduct research to identify market opportunities, understand customer needs, and position our products as a leader in the market.
- Develop and maintain a product roadmap that addresses both short-term deliverables and long-term strategy.
- Engage directly with enterprise customers to gather feedback, understand their challenges, and iterate on product features accordingly. Oversee the creation of technical documentation, ensuring clarity and comprehensibility for a range of stakeholders.
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