AI/ML Senior Software Engineer, Data Optimization and Platform
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with data structures and algorithms in Python.
- 3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
- Experience with data analysis, data optimizations and data evaluations.
Preferred qualifications:
- Master's degree or PhD in Computer Science or a related technical field.
- 5 years of experience working in a complex, matrixed organization.
- Experience in Gen AI.
- Experience in data optimization and data platforms.
- Experience with research to production.
About the job
Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. 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 Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. 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 be a part a team operating in the Gemini Era, where AI is profoundly data-centric, the “quality” data used for training, fine-tuning, or Retrieval-Augmented Generation (RAG) has greater impact one end product performance than almost anything else.
Your mission is to improve the time to model quality for users, achieved by bringing Data Optimization techniques to a broad audience through integrated tools and platforms. In this role, the tooling automatically and efficiently applies state-of-the art data optimization techniques, and runs structured ablations to demonstrate to users which ones work best for their use case, and deliver insights on how to improve further. You will collaborate with key product teams across Google.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.
Responsibilities
- Scale state-of-the-art data optimization techniques (including those from Google Research) to enhance the performance and quality of ML models, while heavily shaping the technical direction of data platforms for model tuning across multiple product areas.
- Establish technical relationships with multiple product areas to define strategy and drive technical directions.
- Leverage existing assets and state-of-the-art techniques from Google Research to accelerate model tuning velocity for teams like Bard and other product areas fine-tuning Gemini models.
- Collaborate with Research teams and ML practitioners to identify, build and iterate on engineering tools, processing pipelines, data optimization techniques, integration with existing workflows, user interfaces and supporting users adoption.
- Apply research and enage directly with users to advance Google’s goal of making AI helpful for everyone.
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