Senior Software Engineer, Context Engineering, Fuchsia

Google•Mountain View, CA

9d•$174,000 - $252,000

About The Position

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. Context Engineering is the team responsible for building the context infrastructure, document evaluation systems, and agent-ready documentation platforms that power developer and Artificial Intelligence agent velocity across our team and Fuchsia. The Software Engineer (Context Engineer) role replaces traditional, manual technical writing with scalable engineering automation under the charter of “Task success through engineered context.” The role directly addresses Fuchsia’s need for continuous context quality infrastructure delivering pre/post-commit doc drift detection, the Fuchsia eXecutable (fx) knowledge graph code-to-docs knowledge graph, and autonomous agent-driven doc remediation. By tying impact directly to agent task success rates, reduced token bloat, and accelerated time-to-first-edit, this role provides the foundational plumbing required to sustain high-velocity agentic development across our team. Fuchsia is a modern, open source operating system that is simple, secure, updatable, and performant. It’s a general purpose OS, designed to power an ecosystem of hardware and software, and provides core operating system functions like system resource management, a driver framework, and software abstractions.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google [https://www.google.com/about/careers/applications/benefits/].

Requirements

Bachelor’s degree or equivalent practical experience.
5 years of experience with software development in either Python or Go 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 developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.

Nice To Haves

Master's degree or PhD in Computer Science or related technical field.
5 years of experience with data structures and algorithms.
Experience developing, deploying, or fine-tuning agentic loops (e.g., tool-calling, multi-step planning, automated self-correction, or IDE coding assistants like Jetski).
Experience integrating automated bots, sidecars, or presubmit checks into developer workflows across review platforms like Gerrit or Critique.
Experience building quantitative evaluation harnesses to measure agent task success, turn efficiency, token economics, or semantic drift.
Familiarity with Abstract Syntax Trees (ASTs), static analysis, symbol indexing, or building code-to-documentation knowledge graphs (e.g., mapping symbols, FIDL/APIs, and manifests to guides).

Responsibilities

Write and test product or system development code.
Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
Build the software systems, automated pipelines, evaluation harnesses, and knowledge graphs that transform documentation from static reference material into dynamic, machine-actionable context that accelerates AI agents and human developers across the Fuchsia ecosystem