Minimum qualifications: Bachelor’s degree or equivalent practical experience. 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree. 1 year 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. 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging). Preferred qualifications: Master's degree or PhD in Computer Science or related technical fields. 2 years of experience with data structures and algorithms. Experience with design and architecture and testing/launching software products. Experience in building and developing large-scale infrastructure. Experience in either C++ and Python. 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. The mission of the Agent Platforms team is to introduce excellent AI/ML capabilities (including GenAI, MLOps, etc.) to Google Cloud, and make them super reliable, performant, cost effective, flexible, and simple for Google Cloud customers. This team will be focusing on bringing the best of Google’s machine learning investments, including Large Language Model (LLM) Serving and Tuning and more, along with Cloud and Operational Support Systems (OSS) technologies to provide the ML infrastructure, tools and services that powers Google Cloud customers. We are also tasked with providing guardrails and safety gates at all stages of the both the development and deployment cycle of the entire Vertex engineering organization to ensure that customers never experience a release that has broken, degraded or otherwise poor functionality. The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. Poland: zł280000 - zł287000 (PLN) + 15% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Engineer and deploy scalable, user-friendly AI/ML platform capabilities that power the next generation of AI. Design, implement and deploy solutions that guarantee the reliability, performance and quality of our AI offerings and serving stack. Define safeguards, control and quality gates for the development and release process of the whole Vertex First-Party (1P) product set. Solve intricate, ambiguous problems that span multiple teams, making an impact on the platform's resilience. Reduce technical chaos by establishing best practices, improving system architecture across multiple components, and fostering a culture of high availability and performance optimization at scale.