Software Engineering Manager II, AI/ML GenAI, Google Cloud Compute

Software Engineering Manager

II, AI/ML

GenAI, Google Cloud Compute corporate_fare Google place Kirkland, WA, USA bar_chart Advanced Advanced Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain. info_outline X In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees.

Benefits for this role include:

Health, dental, vision, life, disability insurance

Retirement Benefits:

401(k) with company match

Paid Time Off:

20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment

Sick Time:

40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks

Baby Bonding Leave:

18 weeks

Holidays:

13 paid days per year Minimum qualifications: Bachelor's degree or equivalent practical experience. 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript). 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 3 years of experience in a technical leadership role. 2 years of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision). 2 years of experience in a people management or team leadership role.

Preferred qualifications:

Master's degree or PhD in Engineering, Computer Science, or a related technical field. 3 years of experience working in an organization involving cross-functional, or cross-business projects. About the job Responsibilities Set and communicate team priorities that support the broader organization's goals. Align strategy, processes, and decision-making across teams. Set clear expectations with individuals based on their level and role and aligned to the broader organization's goals. Meet regularly with individuals to discuss performance and development and provide feedback and coaching. Develop the mid-term technical vision and roadmap within the scope of your (often multiple) team(s). Evolve the roadmap to meet anticipated future requirements and infrastructure needs. Design, guide and vet systems designs within the scope of the broader area, and write product or system development code to solve ambiguous problems. Lead the design of GenAI solutions, optimize ML infrastructure, and guide the development of data preparation and model optimization strategies.