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Job Description
Minimum qualifications: Bachelor's degree in Electrical Engineering, Computer Science, with an emphasis on AI/ML, a relevant technical field, or equivalent practical experience. 5 years of experience in software development. 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 2 years of experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision). Preferred qualifications: 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture. Experience with agentic systems, ML domains such as reinforcement learning, image analysis or advanced statistics. Understanding of machine learning fundamentals. Ability in establishing and tracking meaningful metrics for ML system performance and reliability. Passion for coaching and mentoring team members.
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. With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions. We are a small, dynamic team at the forefront of generative AI. Our mission is to automate device operations across a variety of form factors (including phones, laptops, and tablets) and to derive insights from acquired data. Inspired by recent AI advancements, we aim to build agents capable of operating devices, at scale and with high efficiency and accuracy. We foster a collaborative environment where team members can learn from each other and contribute together. ChromeOS delivers quality computing at scale to provide universal and unfettered access to information, entertainment, and tools. Our mission is to empower anyone to create and access information freely through fast, secure, simple, and intelligent computing. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. Poland: zł364000 - zł374000 (PLN) + 15% bonus target + equity + benefits Learn more about benefits at Google.
Responsibilities Design, develop, deploy, and maintain scalable machine learning systems for our device automation and app analysis platforms, with a focus on generative and agentic AI. Enhance and refine our AI-powered agents to improve their understanding and execution capabilities across various form factors. Define, establish, and track Key Performance Indicators (KPIs) and metrics to evaluate and iterate on the performance, quality, and reliability of our ML systems. Collaborate closely with other engineers in a fast-paced environment. Provide technical leadership, and mentor team members on machine learning best practices, fostering their growth and development.
Key Responsibilities
Design, develop, deploy, and maintain scalable machine learning systems for device automation and app analysis platforms.
Enhance and refine AI-powered agents to improve understanding and execution capabilities.
Define, establish, and track KPIs and metrics to evaluate ML system performance and reliability.
Collaborate closely with other engineers in a fast-paced environment.
Provide technical leadership and mentor team members on machine learning best practices.