Staff Software Engineer, Machine Learning, Geo
Google Bengaluru, Karnataka, India
Software Development · 10,001+ employees
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About the role
Define the multi-year machine learning roadmap and lead a team of engineers to architect and optimize large-scale inference models. You will advocate for scalable evaluation frameworks and drive the transition to unified joint-modeling architectures.
What they look for
Requirements
Requires a bachelor's degree and at least 8 years of software development experience, including 5 years in machine learning design and deployment. Candidates must demonstrate technical leadership, experience with generative AI, and the ability to influence cross-functional stakeholders.
Full description
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience with ML design and end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
- 5 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.
- Experience in a technical leadership role, managing a team of engineers.
- Experience with Generative AI (e.g., Large Language Models (LLMs), diffusion models), development workflows, and use cases.
Preferred qualifications:
- Master's degree or PhD in Machine Learning, Artificial Intelligence, Computer Science, or a related quantitative field.
- 8 years of experience with data structures and algorithms.
- Experience designing automated evaluation frameworks and experimentation for generative AI and ML systems.
- Experience in one or more adjacent domains including Content Moderation, Conversational AI, Multimodal Fusion, or Truth Synthesis systems.
- Experience in people management within cross-functional environments.
- Ability to align and influence stakeholders across multiple engineering, product, and operations organizations.
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 Geo team is focused on building the most accurate, comprehensive, and useful maps for our users, through products like Maps, Earth, Street View, Google Maps Platform, and more. Every month, more than a billion people rely on Maps services to explore the world and navigate their daily lives.
The Geo team also enables developers to use the power of Google Maps platforms to enhance their apps and websites. As they plot a course for the future of mapping, they are solving complex computer science problems, designing beautiful and intuitive product experiences, and improving our understanding of the real world. Responsibilities:
- Define the multi-year ML direction, modeling roadmap, and shared objectives and key results with partners while leading, mentoring, and growing a team of engineers.
- Architect and optimize sub-second ML/Large Language Models (LLM) inference and agentic reasoning loops enabling real-time intent resolution, adaptive follow-up questioning, and moderation transparency.
- Lead the transition from fragmented models to unified joint-modeling architectures.
- Advocate scalable evaluation frameworks spanning structured facts and unstructured claims, active learning pipelines, and synthetic data generation to maximize model performance.
- Cultivate engineering excellence across the organization.
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