Staff Software Engineer, AutoCloud, Context and Memory

About the role

MINIMUM QUALIFICATIONS:

• Bachelor's degree in Computer Science, AI/ML, Data Systems, Information

Retrieval, a related technical field, or equivalent practical experience.

• 8 years of experience in software development.

• 5 years of experience testing, and launching software products, and 3 years

of experience with software design and architecture.

• 5 years of experience leading ML design and optimizing ML infrastructure

(e.g., model deployment, model evaluation, data processing, debugging, fine

tuning).

• 2 years of experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large

Vision Models) or with GenAI-related concepts (language modeling, computer

vision).

• 2 years of experience building infrastructure on cloud platforms.

PREFERRED QUALIFICATIONS:

• Master’s degree or PhD in Engineering, Computer Science, or a related

technical field.

• Experience building low-latency, high-availability distributed storage

systems and APIs on major cloud platforms.

• Expertise in agent memory (working/episodic), context caching, token pruning,

vector search, and knowledge graphs.

• Ability to define technical roadmaps, author comprehensive design docs, and

align multi-organization stakeholders. Demonstrated skill in coaching

engineers and clearly communicating complex architectures to leadership and

research partners.

• Track record in hybrid search, graph databases, and querying complex cloud

telemetry and topology.

• Background in engineering secure, multi-tenant cloud architectures with

strict data isolation and compliance controls.

ABOUT THE JOB:

Google Cloud's software engineers develop the next-generation technologies that

change how billions of users connect, explore, and interact with information and

one another. 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 Cloud's needs with opportunities to switch teams and projects as you and

our fast-paced business grow and evolve. You will anticipate our customer needs

and be empowered to act like an owner, take action and innovate. 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.

AutoCloud is Google Cloud’s autonomous, AI-powered cloud management portfolio.

We are transforming how enterprise customers design, deploy, operate,

investigate, and optimize their workloads and infrastructure across Google Cloud

Platform (GCP). In this role, you will be the principal technical authority

guiding the design of scalable memory architectures, solving complex state

retrieval issues, and partnering with Principal Engineers, researchers across

DeepMind, and partner teams across Google Cloud to deliver a high-precision,

low-latency, and secure context platform for autonomous operations.

Google Cloud accelerates every organization’s ability to digitally transform its

business and industry. We deliver enterprise-grade solutions that leverage

Google’s cutting-edge technology, and tools that help developers build more

sustainably. Customers in more than 200 countries and territories turn to Google

Cloud as their trusted partner to enable growth and solve their most critical

business problems.Individual pay is determined by factors including job-related

skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

• Own the end-to-end architecture, technical roadmap, and core goal for agent

memory systems, dynamic context synthesis pipelines, graph-based cloud

representations, and hybrid search/RAG platforms.

• Lead the design and implementation of low-latency context caching, token

compression/pruning strategies, working memory buffers, and long-term

episodic knowledge stores for autonomous agents.

• Architect high-throughput, low-latency distributed systems and automated

benchmarking frameworks to ensure sub-second cloud state aggregation, high

retrieval recall, and hallucination mitigation.

• Ensure all context and memory subsystems meet stringent enterprise-grade

multi-tenancy standards, tenant data isolation policies, compliance mandates,

and fine-grained access controls.

• Partner across research (e.g., DeepMind) and platform service teams to

standardize shared context models and APIs, while mentoring engineers and

upholding architectural review standards.