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Senior Software Engineer, Generative AI, Gaming
GoogleBengaluru, Karnataka, IndiaSenior5+ years
Apply 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 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, optimization, data processing, debugging).
- 1 year of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical field.
- Experience deploying, serving, and optimizing generative AI models (LLMs, Vision-Language Models, or Diffusion Models) in high-throughput, low-latency production environments.
- Experience in adapting LLMs or foundational models to comprehend spatial environments, physical constraints (gravity, momentum, collisions), and material properties.
- Expertise in optimizing memory bandwidth, KV-caching, and token routing—specifically to support long-context, multi-agent game loops without latency spikes.
- Proven track record of engineering automated evaluation harnesses, reward models, or testing frameworks (e.g., LLM-as-a-judge, visual regression testing, RLHF) to benchmark model quality, latency, and performance.
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.
We are Machine Learning (ML) specialists powering a gaming platform, and we are on a mission to build YouTube for gaming. Just as YouTube democratized video creation and instant global sharing, we are building a platform where anyone can come in, effortlessly generate immersive playable games using Generative AI, and instantly share their creations with a global community of players.
This is a unique opportunity to build an innovative GenAI product from zero to one. We are looking for a highly motivated Generative AI Engineer to architect the breakthrough GenAI creation engine.
In this role, you will lead the technical transition from foundational research to real-time production, building sub-second 2D/3D asset pipelines, developing a specialized Foundation Gaming Language Model, and pioneering autonomous agentic playtesting harnesses.
Responsibilities
- Design industry-first evaluation suites and reward models (using RLHF and gameplay telemetry) 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 spatial and interactive blind spots of standard LLMs by fine-tuning models with deep domain knowledge in 3D mathematics, engine architectures and physical reasoning (gravity, momentum, collisions), 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 complex, fully rigged 3D meshes (using lossless O-Voxel representations).
- Design intelligent, multi-agent playtesting frameworks where AI agents simulate playthroughs of code and levels to rigorously verify physical logic, boundary constraints, and objective playability before release.
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