Software Engineering Manager, AI/ML, Google Labs

Google•Mountain View, CA

1d•$207,000 - $300,000

About The Position

The Software Engineering Manager provides technical and people leadership for engineers and major projects. The role manages project goals, contributes to product strategy, supports the development of the team, and guides engineers in building and optimizing software. Managers oversee engineers across multiple teams and locations, large product budgets, and deployment of large-scale projects across international sites. This position is in Google Labs, a group that incubates early-stage efforts in support of Google’s mission to organize the world’s information and make it universally accessible and useful. Labs explores and applies new technologies to develop solutions that could transform how users interact with Google, and aims to deliver impactful products and capabilities over longer timeframes. The role focuses on specialized machine learning areas, including leading ML solution design and implementation, optimizing ML infrastructure, and guiding model optimization and data processing strategies.

Requirements

Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience with speech/audio, reinforcement learning, ML infrastructure, or specialization in another ML field.
5 years of experience leading ML design and optimizing end-to-end machine learning, including areas such as model deployment, model evaluation, data processing, debugging, or fine-tuning.
3 years of experience in a technical leadership role.
2 years of experience in a people management or team leadership role.

Nice To Haves

Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
3 years of experience working in a complex, matrixed organization involving cross-functional or cross-business projects.

Responsibilities

Set and communicate team priorities that support broader organizational goals, and align strategy, processes, and decision-making across teams.
Set clear expectations for individuals based on their level and role and organizational goals; meet regularly with individuals to discuss performance and development and provide feedback and coaching.
Develop the mid-term technical goal and roadmap across multiple teams, evolving it to meet anticipated future requirements and infrastructure needs.
Design, guide, and vet systems designs within the broader area, and write product or system development code to solve ambiguous problems.
Lead the design and implementation of solutions in specialized machine learning areas, optimize ML infrastructure, and guide model optimization and data processing strategies.