Staff Software Engineer, Applied Machine Learning
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development and with data structures/algorithms.
- 5 years of experience building and architecting large-scale, production quality Machine Learning (ML) systems.
- 5 years of experience with AI/ML algorithms and tools, Large Language Models (LLM) or other multimodal foundation models, and natural language processing.
- 5 years of experience in distributed development and large-scale data processing.
- Experience coding in either C++ or Python.
Preferred qualifications:
- 3 years of experience in building model optimization pipelines using quantization and distillation techniques.
- 3 years of experience integrating ML, neural networks, including sequence/transformer modeling in other domains like perception or speech, or Generative AI into commercial products.
- Experience in a technical leadership role leading project teams and setting technical direction.
- Experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
- Ability to contribute in the Machine Learning (ML) community through publications, open-source contributions, or conference participation.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
In this role, you will lead the development of our Generative Artificial Intelligence (AI) development platform. You will play a pivotal role in ensuring that Google products are able to seamlessly incorporate the latest Generative AI models into their software development lifecycle. You will build a unified toolchain that helps Google Product teams build and launch Generative AI-powered products. You will partner with Google DeepMind and Core Infrastructure teams across Google. You will collaborate closely with researchers building the next generation of Generative AI.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
- Drive the platform goal to incorporate fine-tuning, multi-agent systems, prompt engineering, model optimization into the Google product development lifecycle.
- Develop and maintain our products, written in C++ and Python and ensure it complies with Google-wide coding and testing standards.
- Design and implement customer requested Generative AI features through multiple stages, requirements gathering, proposing design and building agreement among the key stakeholders.
- Collaborate with stakeholders, including other Google product areas, Google Research, and DeepMind organizations to identify emerging technology related to Artificial Intelligence (AI) and Generative AI and develop a strategic plan for translating these into practical solutions for Google products.
- Provide technical leadership and mentorship to team members, fostering their growth and development in the applied Machine Learning (ML) space.
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