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

Responsibilities

You will architect closed-loop recursive self-improvement pipelines to autonomously refine agent instructions and trajectories. Additionally, you will build multi-agent systems to optimize model context, improve inference efficiency, and bridge inference-time engineering with model post-training.

Requirements

Candidates must have a bachelor's degree and at least 8 years of software development experience, including 5 years in ML design and LLM agent architectures. Preferred qualifications include technical leadership experience and expertise in building recursive self-improvement or automated prompt refinement frameworks.

Skills

• Software Development
• Machine Learning
• LLM
• Multimodal Agent Architectures
• Agentic AI Frameworks
• Automated LLM Evaluation
• Data Structures
• Algorithms
• Technical Leadership
• Recursive Self-Improvement
• Prompt Optimization
• Instruction-following
• Multimodal Reasoning
• Inference Efficiency
• System Design

Benefits

• Bonus
• Equity
• Health Insurance
• Retirement Plan

About this 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.

About Google

Software Development · 10,001+ employees · headquartered in Mountain View, CA