Senior Software Engineer, AI/ML, Quality, Applied AI
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development 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).
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical field.
- Experience with LLM tuning methodologies (e.g., LoRA, fine tuning).
- Experience in developing and deploying models/solutions on cloud platforms (e.g., Google Cloud).
- Experience in Natural Language Processing (NLP) with a focus on conversational AI or text generation.
- Experience in Prompt Engineering, building or utilizing LLM-powered auto-evaluation and optimization systems.
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 manage information at a 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.As a part of the Customer Engagement Suite (CES), you will work on an AI-powered customer service solution on the Google Cloud platform serving major contact center service providers, large enterprises, and Google's own products like YouTube.
You will work on agent assist, the core pillar built by the CES agent assist team, which is a high-impact product designed to significantly boost human agent productivity and customer satisfaction while dramatically reducing operational costs. You can find more information about Agent Assist on our product page and is now powered by state-of-the-art LLMs and Agentic AI. You will leverage LLMs and agentic AI techniques, in close collaboration with Conversational Agent, Vertex LLM and GDM teams.Applied AI builds conversational agents deployed at a large-scale that achieve very meaningful results in the real world. Some examples include the customer agent built for large call center environments, to fast food ordering managed by our Food AI agent. The team is transforming how enterprises connect with customers through the power of AI. We also offer unique experiences for team members where you get to work directly with the model builders (Google DeepMind / Vertex), learn and work with brilliant AI leaders, and have access to Global 1000 customers via our existing Google Cloud relationships. The opportunity in this space is tremendous.
Responsibilities
- Design and iteratively refine high-quality prompts to ensure accurate, context-aware responses within customer support environments.
- Apply techniques to efficiently customize base models for specific enterprise use cases and industry nuances.
- Develop Large Language Model (LLM) powered auto-evaluation methodologies to rigorously measure model quality, relevance, and safety at scale.
- Utilize frameworks like ADK for rapid feature exploration while optimizing infrastructure for low-latency inference and model-serving pipelines.
- Partner cross-functionally with PM, CE, and PSO teams to lead enterprise customer onboarding and translate direct feedback into product improvements.
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