Staff Applied ML Software Engineer, Recommendations, YouTube - Dallas, Texas, US
About the role
Overview
If the following job requirements and experience match your skills, please ensure you apply promptly.
As a technical leader on high-impact projects at Google, you will guide cross-functional engineering teams to deliver production-grade recommender systems. You will own priorities, deadlines, and deliverables, ensuring alignment across teams on goals and timelines. You’ll coach a distributed team and shape the design of model architectures and ML infrastructure. You will analyze data and logs to drive ML-driven product improvements. This role blends leadership with hands-on ML system design to push a cutting-edge recommendation agenda at scale.
Compensation / Benefits
• bonus target
• equity
• benefits
• competitive salary
• growth opportunities
Responsibilities
• Provide technical leadership on high-impact projects
• Manage project priorities, deadlines, and deliverables
• Facilitate alignment across teams on goals, outcomes, and timelines
• Coach and influence a distributed team of engineers
• Lead design and implementation of recommendation systems and optimize ML infrastructure
• Guide development of model architecture
• Analyze data and logs to drive ML-powered product improvements
Key requirements
• Bachelor in Computer Science or related field; equivalent practical experience
• 8 years of software development experience
• 5 years building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production
• 5 years of ML design and ML infrastructure xklufbp (model deployment, evaluation, data processing, debugging, fine tuning)
• 3 years of Applied Machine Learning experience
• Experience with publications in ML or related field
• Leadership and coaching
• Cross-functional collaboration
• Communication and alignment across teams
• Recommendation systems (retrieval, prediction, ranking, embedding)
• ML infrastructure (deployment, evaluation, data processing, debugging, fine tuning)
• Applied machine learning