Senior Machine Learning Engineer, Vertex Agent Quality
Google•Published 4 hours ago•First seen 4 hours ago
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.
In this role, you will collaborate with Google DeepMind (GDM) to advance foundational model agentic capabilities, partner with Gemini Enterprise to elevate Core Assistant quality, and work with Agent Builder to drive simulation and optimization features.
In this role, you will establish evaluation frameworks by building rigorous agentic benchmarks and high-fidelity Reinforcement Learning (RL) environments to assess and enhance foundational model capabilities. You will drive quality improvements by optimizing enterprise AI Agent end-to-end quality via novel orchestration, advanced prompt/context engineering, and model tuning. You will develop core features by engineering algorithms and features for agent simulation, evaluation, and optimization within Vertex Agent Builder (focusing on ADK and Agent Engine).The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're behind Google's groundbreaking innovations, empowering the development of AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
In this role, you will collaborate with Google DeepMind (GDM) to advance foundational model agentic capabilities, partner with Gemini Enterprise to elevate Core Assistant quality, and work with Agent Builder to drive simulation and optimization features.
In this role, you will establish evaluation frameworks by building rigorous agentic benchmarks and high-fidelity Reinforcement Learning (RL) environments to assess and enhance foundational model capabilities. You will drive quality improvements by optimizing enterprise AI Agent end-to-end quality via novel orchestration, advanced prompt/context engineering, and model tuning. You will develop core features by engineering algorithms and features for agent simulation, evaluation, and optimization within Vertex Agent Builder (focusing on ADK and Agent Engine).The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're behind Google's groundbreaking innovations, empowering the development of AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
The US base salary range for this full-time position is $166,000-$244,000 + 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
- Write and test product or system development code.
- Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
- Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
- 1 year of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
- Experience with AI Agent development.
- Experience in agent quality and model tuning.
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical field.
- 5 years of experience with data structures and algorithms.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
- 1 year of experience in a technical leadership role.