Staff Software Engineer, Agentic, Optimization, Recursive Self-Improvement

About the role

MINIMUM QUALIFICATIONS:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience with ML design and with LLM/multimodal agent

architectures.

• Experience utilizing agentic AI frameworks or developer kits to build and

deploy autonomous AI systems.

• Experience in automated LLM evaluation (e.g., LLM-as-a-Judge, multi-turn user

simulation, or Pareto optimization).

• Experience integrating generative AI tools or LLM interfaces into workflows

and designing metrics for the AI tools.

PREFERRED QUALIFICATIONS:

• 8 years of experience with data structures and algorithms.
• 3 years of experience in a technical leadership role leading project teams

and setting technical direction.

• 3 years of experience working in a complex, matrixed organization involving

cross-functional, or cross-business projects.

• Experience building Recursive Self-Improvement, automated prompt, or agent

refinement frameworks (e.g., DSPy, OPRO, TextGrad, Reflexion, STaR).

• Experience elevating performance on benchmarks for instruction-following,

multimodal reasoning, and agents.

• Experience with prompt compression, structured output enforcement, or

context-to-weight distillation.

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 looking for a Staff Software Engineer to help our efforts in Recursive

Self-Improvement (RSI), Automated Prompt Optimization (APO), and Agentic prompt

simplification. We are working towards ensuring that multimodal journeys in

Search are fully onboarded onto RSI, and we also motivate horizontal

improvements across Search from our learnings.

In this role, you will help set and execute the technical direction as part of a

strong team for autonomous, closed-loop agentic systems that continuously

optimize, compress, and modularize system instructions and agent

trajectories—improving instruction-following fidelity, reasoning quality, and

inference efficiency at scale.

In Google Search, we're reimagining what it means to search for information –

any way and anywhere. To do that, we need to solve complex engineering

challenges and expand our infrastructure, while maintaining a universally

accessible and useful experience that people around the world rely on. In

joining the Search team, you'll have an opportunity to make an impact on

billions of people globally.

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:

• Partner with senior leads to architect closed-loop RSI pipelines that

autonomously diagnose failure modes, refine instructions/trajectories,

synthesize targeted training/eval data, and verify gains without manual

intervention.

• Investigate and build automated multi-agent systems that audit, compress, and

de-conflict complex model context—maximizing token efficiency and inference

speed while preserving behavioral and safety guardrails.

• Bridge inference-time context engineering with model post-training (SFT/RL)

to distill complex rules into model weights.

• Advance automated optimization methods for production multimodal and

reasoning systems.

• Help define the multi-quarter roadmap across Engineering and Research teams,

establishing rigorous automated evaluation gates.