Minimum qualifications:
- Bachelor’s degree 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 with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), machine learning infrastructure, or specialization in another machine learning field.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 3 years of experience in a technical leadership role guiding project teams and setting technical direction.
- 3 years of experience working in a 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.
Our special projects team within Google Tech and Society is dedicated to mechanistic artificial intelligence research. We are a collaborative group with interconnections to research and development teams throughout the company. Our focus is on the basic science of mechanistic interpretability, striving to reverse-engineer the internal computations of large language models to ensure their safety, alignment, and reliability. We push beyond traditional approaches to understand the compositional and structural mechanisms within models.
The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about
benefits at Google.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about
benefits at Google.
Responsibilities
- Design, architect, and implement cognitive architecture primitives that form the foundation of future agentic artificial intelligence systems.
- Guide the development and integration of these cognitive systems, leveraging agentic programming tools (e.g., Antigravity/Jetski, full-stack vibe coding) to accelerate production and deployment.
- Partner with stakeholders, product managers, and research scientists to ensure system implementation is aligned with objectives and ethical guidelines.
- Serve as a technical leader, mentoring engineers and researchers, and setting standards for engineering excellence and scalable artificial intelligence systems.
- Deliver continuous improvement in core infrastructure and development workflows, and serve as a team foundation on scaled evaluation and the piloting of artificial intelligence capabilities.
