Minimum qualifications:- Bachelor's degree in Computer Science, Machine Learning, Mathematics, Statistics, a related technical field, or equivalent practical experience.
- Experience programming in Python or C .
- Experience with machine learning, algorithm design, data structures, and distributed software systems.
- Experience taking technical projects or machine learning systems from conceptual formulation to implementation and deployment.
Preferred qualifications:- Experience developing, fine-tuning, or optimizing foundation models including techniques such as RLHF/RLAIF, supervised fine-tuning, parameter-efficient tuning, or inference optimization.
- Experience with personalization, adaptive systems, user modeling, retrieval-augmented generation, or agentic memory architectures.
- Experience with modern machine learning frameworks and model training or serving infrastructure.
- Experience collaborating across research and product boundaries to co-design technical architectures.
About the jobAt Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
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 .
Responsibilities - Design, train, and optimize foundational algorithms and machine learning systems (e.g., personalized model adaptation, agentic workflows, contextual memory architectures, dynamic prompt optimization, and multimodal reasoning).
- Lead end-to-end technical development from algorithmic design and experimental prototyping to production-grade architecture and scaled serving infrastructure.
- Partner directly with engineering and product teams to integrate and harden core technologies within production environments (e.g., Project Helix, agent workspaces, and intelligent system integrations).
- Formulate novel automated and human-in-the-loop evaluation methodologies to measure capability gains, latency/compute efficiency, alignment, and personalization fidelity.
