AI/ML Staff Software Engineer, AI Data
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience programming in C++, Java, Python, Kotlin or Go.
- 5 years of experience testing, and launching software products.
- 3 years of experience with software design and architecture.
- Experience integrating generative AI tools or LLM interfaces into workflows.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures/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.
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.
Frontier model progress across Gemini relies on evaluation quality, velocity, and trustworthiness. As a Staff Software Engineer, you will manage, discover, and implement techniques that accelerate evaluation and training data development. Sitting at the intersection of DeepMind and AI Data, you will build systems that mine execution logs, agent trajectories, and production artifacts into benchmarks and training data that self-improve over time. You will turn ad-hoc evaluations into automated data flywheels—combining programmatic verifiers, difficulty modeling, and hermetic sandboxes to measure and unlock complex reasoning.
Our mission is to improve the time to model quality for our users. We do so by bringing Data Optimization techniques to a broad audience through integrated tools and platforms. Our tooling applies data optimization techniques, and runs structured ablations to demonstrate to our users which ones work best for their use case, and deliver insights on how to improve further.
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.
Responsibilities
- Research and implement novel techniques for accelerated evaluation creation, including automated task synthesis, programmatic verifiers, and model-assisted rubric induction.
- Architect pipelines to mine execution logs, agent traces, and runtime artifacts into high-fidelity, future-proof evals and training data that continuously self-improve.
- Design and build robust benchmarks evaluating complex, long-horizon agentic reasoning across code, math, and specialized domains; optimize for climbability, discriminability, and zero contamination.
- Build and scale platform software that automates quality gates—such as AutoRaters and automated loss-pattern root-cause analyzers.
- Provide technical direction and architecture across AI Data and partners such as GDM and Cloud AI. Align distributed engineering teams and establish flexible approaches reusable across all types of benchmarks.
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