Google

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in one or more programming languages, and with data structures/algorithms.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
• Technical leadership experience in architecture, infrastructure design, and machine learning infrastructure.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• Experience working with or designing systems for Large Language Models (LLMs) and advanced AI architectures.
• Deep understanding of data optimization techniques, machine learning model quality improvement methodologies, or ablation experimentation.
• Familiarity with Google's internal ML ecosystem (e.g., Gemini, One Recipe, AIFlow) or similar large-scale distributed ML frameworks.
• Exceptional communication and collaboration skills, with the ability to articulate complex technical concepts to audiences and drive consensus.
• Demonstrated ability to define and execute a compelling technical roadmap for a product or platform, anticipating future needs and industry trends.

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.

Our team is focused on improving the time to model quality by offering advanced data optimization techniques. We believe high-quality data is essential for training, fine-tuning, and to achieve optimal AI product performance.

We are developing integrated tools and platforms that automate data optimization, provide clear performance insights through structured analysis, and suggest further improvements. Our goal is to connect data and ML, delivering the high-quality data needed for excellent ML applications. We aim to enhance processing and analysis throughout the ML lifecycle, enabling users to achieve model performance quickly and efficiently.

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.

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

• Lead the strategic evolution of our machine learning ablation experimentation orchestration infrastructure, adapting it from classical ML paradigms to seamlessly integrate with Large Language Models (LLMs). This includes working cross-functionally to ensure capabilities.
• Advocate for our users, driving the adoption of our tools and platforms by meticulously optimizing user journeys. Create excellent, intuitive, and highly effective experiences that empower ML practitioners to achieve their goals efficiently.
• Provide exceptional technical leadership and guidance to a team of talented engineers. Mentor team members, fostering their growth, and ensuring high-quality technical output and best practices across the team.
• Adapt to rapid changes in the AI and ML landscape.