Senior Staff ML Engineer, Recommendations

Google LLC; San Bruno (CA); On-site; USD 207,000 - 300,000; Full time

Job summary

Google LLC's YouTube team seeks a Staff Applied ML Software Engineer for Recommendations. You will lead design and deployment of advanced recommender systems, ML infrastructure, and model architectures, with a strong emphasis on research and production-grade impact.

Ideal candidates bring deep ML expertise, publications in ML, and a track record of guiding multi-disciplinary teams in a fast-paced environment. In-person interviews are common for this role at YouTube.

Qualifications

• Bachelor's degree or equivalent practical experience in CS or related field.
• 8 years of software development experience.
• 5 years in building/deploying recommendation systems in production.
• 5 years ML design/infrastructure experience.
• 3 years in Applied ML.
• Publications in ML or related fields.

Responsibilities

• Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
• Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.
• Lead the design and implementation of recommendation systems, optimize ML infrastructure, and guide the development of model architecture.
• Be comfortable analyzing data, drawing conclusions from the logs and using ML to improve the product.

Skills

Software development experience

Recommendation systems

ML design & infra

Applied ML experience

Publications in ML

Leadership in tech projects

Education

Bachelor's degree in CS or related field

Master's or PhD in CS or related field

Job description

Google LLC's YouTube team seeks a Staff Applied ML Software Engineer for Recommendations. You will lead design and deployment of advanced recommender systems, ML infrastructure, and model architectures, with a strong emphasis on research and production-grade impact.

Ideal candidates bring deep ML expertise, publications in ML, and a track record of guiding multi-disciplinary teams in a fast-paced environment. In-person interviews are common for this role at YouTube.