Staff Software Engineer, Generative AI, Core Machine Learning

Staff Software Engineer, Generative AI, Core Machine Learning corporate_fare Google place Mountain View, CA, USA bar_chart Advanced Advanced Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.

Minimum qualifications:

Bachelor's degree or equivalent practical experience. 8 years of experience in software development. 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 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). 5 years of experience testing, and launching software products. 1 year of experience in building Generative AI or agentic applications.

Preferred qualifications:

Master's degree or PhD in Engineering, Computer Science, or a ML-related technical field. 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. Production experience in applying ML related features to a scalable system. About the job 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 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 . Responsibilities Generalize horizontal tools, including a plug-in Knowledge Store and a Self-Reflection module, into reusable services that any agent can adopt to enable automated learning and improvement. validated design principles to transition deterministic logic from monolithic prompts into efficient, code-based workflows that maximize reasoning value and ensure precise context control. Execute deep architectural reviews and automated trace scanning for high-impact agents to eliminate structural inefficiencies and redundant tool calls in multi-turn reasoning chains. Extend automated prompt optimization frameworks to support multi-step workflows, enabling systematic improvement of both efficiency and quality across the agent ecosystem. Implement code-level safety enforcements and structured tool return patterns within the agent platform to ensure reliability and correctness on deterministic subtasks.