Senior Software Engineer, AI/ML

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 building and shipping technical products. Experience with the area of Generative AI and Large Language Models (LLM).
• Experience developing/launching products/technologies within AI/ML or a related area.

Preferred qualifications:

• Master’s degree or PhD in Computer Science or a related technical field.
• Experience in engineering automated evaluation harnesses, reward models, or testing frameworks (e.g., LLM-as-a-judge, visual regression testing, Reinforcement Learning from Human Feedback (RLHF)) to benchmark model quality, latency, and performance.
• Experience in adapting LLMs or foundational models to comprehend spatial environments, physical constraints (e.g., gravity, momentum, collisions), and material properties.
• Expertise in optimizing memory bandwidth, Key-Value (KV)-caching, and token routine specifically to support long-context, multi-agent game loops without latency spikes.
• Foundational knowledge of 3D linear algebra, spatial reasoning, computational geometry, and real-time graphics.

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.

In this role, you will have an opportunity to build an innovative Generative Artificial Intelligence (GenAI) product from zero to one. You will be a motivated Generative AI Engineer to architect our breakthrough zero-to-one GenAI creation engine. You will lead the technical transition from foundational research to real-time production, building sub-second two-dimensional/three-dimensional (2D/3D) asset pipelines, developing a specialized Foundation Gaming Language Model, and pioneering autonomous agentic playtesting harnesses.The Platforms and Devices team encompasses Google’s various computing software platforms across environments (desktop, mobile, applications), as well as our first party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user’s interaction with computing faster and more seamless, building innovative experiences for our users around the world.

Responsibilities

• Design industry-first evaluation suites and reward models to systematically quantify subjective game quality, balance, and core loop fun, while deploying autonomous multi-agent frameworks to playtest generated code and verify physical logic before release.
• Address the interactive blind spots of standard Large Language Models (LLMs) by fine-tuning models with deep domain knowledge in 3D mathematics, engine architectures, and physical reasoning building frictionless bridges directly into real-time render loops.
• Drive the foundational research and deployment of rapid-iteration generative models leveraging distillation algorithms for sub-second, transparent 2D sprite generation and adaptive-resolution Diffusion Transformers (DiTs) to generate rigged 3D meshes (using lossless O-Voxel representations).
• Design intelligent, multi-agent playtesting frameworks where autonomous AI agents simulate playthroughs of generated code and levels to verify physical logic, boundary constraints, and objective playability before human release.
• Design a foundation model for gaming.

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