Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- Experience integrating generative AI tools or LLM interfaces into workflows.
- Experience with Kubernetes, C , Go programming.
Preferred qualifications:- Master's degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 5 years of experience with software development in Go, Python or Java programming languages.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience with distributed system design of infrastructure software.
- Experience testing, maintaining, or launching software products, and with software design and architecture.
About the jobThe AI industry is rapidly adopting GenAI models due to their versatility and powerful capabilities in creating text, code, images, videos and more via increasingly more complex input context length. However, even the most technically competent businesses and GenAI developers will face challenges in development, scaling and managing the cost and infrastructure required to host such large models. Our Model as a Service (MaaS) aims to provide a much needed new level of abstraction to remove the complexity of development and provide large GenAI models as a scalable, cloud-based service with an enhanced suite of tools to facilitate the use, integration, and model management.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) 20% bonus target equity benefits
Responsibilities - Build infrastructure for model creators to be able to generate business with their models.
- Build a platform for partners to manage their services on Vertex.
- Work with sister teams to bring optimized models and serving techniques (e.g. prefix caching, cache-aware routing, etc.).
- Build infrastructure for efficiently onboarding open and closed source models to Google Cloud.
- Scale and maintain existing Model-as-a-service deployments.
