Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software programming in Python or C++.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- 3 years of experience in a core ML domain, including but not limited to generative AI, Natural Language Processing (NLP), computer vision, speech/audio, reinforcement learning, recommendation systems, or ML infrastructure.
- 3 years of experience with ML infrastructure (e.g., model training, model inference, model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:
- Master's degree or PhD in Computer Science, or a related technical field.
- 5 years of experience with data structures and algorithms; with 2 year of experience in a technical leadership role.
- Expertise in distributed systems architecture and core programming, paired with a sophisticated, nuanced understanding of LLM capabilities, limitations, and failure modes.
- Proven track record of deploying and scaling enterprise-grade LLM-backend applications, RAG system, and agentic systems.
- Knowledge in AI safety, enterprise security, advanced prompt engineering, and scalable model evaluation methodologies.
- Ability to drive technical consensus and engineering excellence for complex, high-ambiguity 0-to-1 initiatives across multiple teams.
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.
With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
As a Senior Software Engineer in the Acceleration Platform team in Singapore, you will architect the next generation of AI-native agentic systems designed to eliminate systemic developer toil at a global scale. You will own the technical goal, moving beyond tool-building to define the very paradigms of how self-sustaining AI resolves complex engineering issues. You will navigate high-ambiguity, zero-to-one initiatives, establishing the gold standard for AI engineering excellence and security-by-design while scaling the technical capabilities of the entire organization through high-leverage mentorship.
The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
Responsibilities
- Lead the design and architecture of highly scalable, fault-tolerant systems where multi-agent networks reason, plan, and execute complex workflows across vast, distributed codebases.
- Define best practices for the team and broader organization. Blend traditional distributed systems architecture with advanced LLM orchestration, complex Retrieval Augmented Generation (RAG) pipelines, and optimization.
- Establish the overarching technical strategy for AI quality and safety. Build automated evaluation frameworks that measure performance, enforce strict security standards, and reliably mitigate at scale.
- Manage the most intricate non-deterministic edge cases. Build advanced telemetry and introspection tooling that allows the entire organization to understand, debug, and optimize self-sustaining behavior.
- Drive technical alignment across local pods and global organizations. Mentor junior and mid-level engineers, translate extreme ambiguity into actionable technical roadmaps, and shape the future of AI-driven developer productivity.
