Research Scientist focused on improving the computational efficiency of large-scale Generative AI Models (LLMs, Diffusion Models, Generative Videos) through algorithmic research, model compression, and inference acceleration. The role involves advancing algorithms for serving and inference, innovating training architectures, optimizing deployment pipelines, and collaborating with hardware/software teams. A PhD and publication record are required.

Research Scientist focused on improving the computational efficiency of generative AI models (LLMs, Diffusion Models, Generative Videos) through foundational research in algorithmic efficiency, model compression, and inference acceleration. This role involves innovating algorithms, optimizing model architectures, improving the deployment pipeline (pretraining, tuning, RL), and collaborating with hardware/software teams to optimize inference engines and reduce latency/memory usage.

Research Software Engineer at Google Research focused on pioneering AI research in Singapore, specifically on developing performant, efficient, and capable generative AI models. The role involves abstracting problems, designing solutions, prototyping, profiling, benchmarking, and collaborating with global research and product teams.

Manager of a GenAI Forward Deployed Engineering (FDE) team responsible for leading AI/ML engineers to deploy bespoke agentic solutions within customer environments. The role involves technical mentorship, strategic alignment with Product, Engineering, and Sales, and resolving production-level obstacles for AI adoption in enterprise settings.

Manager of a GenAI Forward Deployed Engineering (FDE) team responsible for leading AI/ML engineers in deploying bespoke agentic solutions within customer environments. The role involves technical mentorship, strategic alignment with Product, Engineering, and Sales leadership, and resolving production-level obstacles related to AI adoption.

Software Engineer on the Acceleration Platform Team building scalable, AI-native agentic systems to automate complex workflows and solve enterprise-scale engineering problems. The role involves leading zero-to-one initiatives, defining AI-first engineering best practices, scaling evaluations and guardrails for AI safety, and building advanced telemetry for debugging and optimizing self-supporting behaviors.

Software Engineer on the Acceleration Platform team in Singapore, responsible for designing and implementing AI-native agentic systems to eliminate developer toil. The role involves leading zero-to-one technical initiatives, building scalable frameworks for self-sustaining AI at an enterprise scale, and mentoring junior peers. Responsibilities include designing systems where AI agents reason and execute workflows, blending traditional software development with prompt engineering and RAG, creating evaluation pipelines for AI performance, and troubleshooting non-deterministic AI behaviors.

Staff Software Engineer at Google focused on Content Safety, Productionization. This role involves safeguarding business-critical products, including GenAI experiences, by designing, building, and scaling content safety solutions like classifiers and multimodal understanding. Key responsibilities include working on agentic workflows for threat understanding and content moderation, leading ML solution design, optimizing ML infrastructure, and guiding model optimization and data processing strategies. The role requires significant experience in software development, ML infrastructure, and ML design, with a focus on productionizing AI for safety.

Staff Software Engineer focused on building and scaling content safety solutions, including agentic workflows and multimodal understanding, for Google's products, with a strong emphasis on ML infrastructure and model optimization.

Forward Deployed Engineer II, Generative AI, Google Cloud. This role involves building and shipping production-grade agentic AI solutions within customer environments, managing integration complexities, data readiness, and state management. The engineer will also build evaluation pipelines and observability frameworks, and provide feedback to product roadmaps. Requires experience with Python/Typescript, building AI solutions, data pipelines with vector databases/RAG, and cloud platforms. Preferred experience with multi-agent frameworks and LLM optimization techniques.

Software Engineering Manager for Google Cloud's ML Compute Services, focusing on optimizing customer AI/ML models on Google Cloud infrastructure. The role involves leading a team, providing technical guidance, partnering with customers on performance, and collaborating with internal teams to enhance AI workload support.

Senior Software Engineer to lead the design and development of end-to-end testing solutions using AI-powered humanoid robots. The role involves creating agents that physically interact with devices to validate user experiences and run exploratory tests, integrating hardware, embedded software, and AI models, and collaborating with AI and robotics teams.

Software Engineer focused on AI internationalization and evaluations for Pixel and Android. Responsibilities include leading R&D for AI feature expansion, quality evaluations, and rater quality using on-device and server-based models. Tasks involve creating auto-raters, ensuring metric consistency, establishing benchmarks, and collaborating with AI feature teams. The role also involves identifying opportunities and leading roadmaps to scale language capabilities and improve model evaluation processes.

Research Scientist role focused on Android security and AI agent safety, developing novel research and agent-powered systems to detect and prevent various forms of abuse. The role involves analyzing codebases using ML and collaborating with internal and external partners.

Research Scientist focused on protecting Android users from abusive apps by developing and publishing novel research on Android security and AI agent safety. The role involves designing neuro-symbolic and agent-powered systems, analyzing codebases using machine learning, and collaborating with internal and external partners to improve defenses against evolving threats.

Senior Software Engineer, Machine Learning, Debug role focused on developing and deploying deep learning models for mosquito-born disease eradication. This involves computer vision for analyzing mosquitoes, statistical modeling for population dynamics, and building production-ready code for scalable deployment, with a focus on optimizing vector control strategies.

Software Engineer role focused on building an AI-native Software Development Lifecycle (SDLC) ecosystem for Android and Pixel. The role involves developing intelligent tools, automation, and evaluation methodologies, leveraging on-device and cloud-based AI technologies, including LLMs and multi-agent systems, to optimize functionality, stability, and release velocity.

Software Engineer III at Google, Enterprise Data Protection team, focused on building security controls to prevent data exfiltration, including threats from autonomous AI agents. The role involves designing and implementing AI-based classification systems and ensuring controls mitigate risks from both human and AI threats.

Security Engineer focused on securing enterprise AI products and features at Google, assessing controls, and mitigating risks associated with AI use. Responsibilities include delivering security assessments and threat models for AI agents, proposing technical guardrails, and assisting with escalations.

This role is for a Senior Security Engineering Manager to lead a new team in Singapore focused on securing Alphabet's enterprise systems and data in the context of AI adoption. The team will develop novel approaches and use AI-motivated automation to manage AI enterprise security risks and enable safe enterprise use of AI, particularly agentic AI workflows.

Software Engineering Manager to lead a team building AI-native, agentic systems for developer productivity, focusing on secure-by-default launches and automating model introspection. This role involves technical leadership, team building, and cross-functional collaboration.

Senior Customer Engineer focused on AI infrastructure, specifically Google Cloud TPUs, for enterprise clients. This role involves designing, deploying, and optimizing AI training and inferencing solutions, advising on ML operations, and supporting sales teams by solving technical challenges related to AI hardware and software stacks.

Customer Engineer role focused on deploying and scaling Gemini Enterprise AI workloads for enterprise customers, involving technical unblocking, deployment planning, and driving adoption of AI-assisted workflows and multi-modal agents.

Senior Software Engineer role focused on building AI-based security controls to prevent data exfiltration, specifically addressing threats from autonomous AI agentic actions within Google's enterprise systems.

Software Engineering Manager for Content Safety, focusing on AI/ML infrastructure and deployment for protecting users from harmful content. The role involves leading teams, setting technical direction, and ensuring the scalability and reliability of ML systems.

Manage a team of software engineers to build and scale content safety solutions using ML/AI techniques for Google's core products, focusing on protecting users from harmful content. This involves technical leadership, people management, and ensuring the production-grade deployment and operation of these safety systems.

Manager for a Data Science Research team focused on protecting Android users from AI-driven threats like malware and impersonation. The role involves leading technical strategy, identifying impactful data science problems, and driving the development of advanced quantitative methods, including ML modeling, to create data-driven products and solutions. This involves cross-functional collaboration with Engineering and Product leaders to translate research into business decisions.

This role focuses on applying advanced quantitative methods and machine learning to protect Android users from abusive applications. The data scientist will conduct end-to-end analysis, develop and evaluate ML models, and collaborate with product and engineering teams to improve safety systems. The work involves handling large datasets in an adversarial environment, aiming to detect and prevent various forms of abuse.

Technical Program Manager for Generative AI Safety at Google, focusing on leading initiatives to expand content safety infrastructure, integrate safety classifiers, and build rapid response capabilities for AI abuse. The role involves partnering with cross-functional leaders to convert threat intelligence into scalable models and technical protections within the serving stack, orchestrating safety engineering teams, and managing global workflows for timely integration and evaluation of safety models for Gemini releases. This role also coordinates with infrastructure teams, generative AI product groups, and foundational model researchers to integrate safety signals into primary models.

Software Engineer role focused on building and scaling content safety platforms using ML infrastructure and responsible AI techniques to protect users from harmful content. The role involves designing, building, and maintaining these platforms, ensuring quality implementations of company-wide standards for Responsible AI, and collaborating with stakeholders. It requires experience in software development, ML infrastructure, and potentially speech/audio or reinforcement learning.

Software Engineer role focused on designing, building, and maintaining content safety solutions for Google's business-critical products. This involves implementing Responsible AI standards, protecting users from harmful content using ML/AI techniques, and ensuring quality through classifiers and classifiers. The role requires production-grade experience in serving infrastructure and product integrations, with a focus on delivering scalable and future-proof solutions.