Lead ML Engineering for Real-Time Recommender Systems

Snap Inc.; Los Angeles (CA); On-site; USD 229,000 - 343,000; Full time

Benefits offered by this job

Paid parental leave

Comprehensive medical coverage

Emotional and mental health support programs

Compensation packages with equity

Job summary

Snap Inc. is seeking a seasoned leader to guide a team of machine learning and software engineers in developing large-scale indexing and ranking systems for Snapchat ads. This role involves collaboration with product teams to define architecture and vision while nurturing a high-performing team by upholding standards of engineering and machine learning excellence.

The ideal candidate will have significant industry experience and a strong understanding of machine learning, recommendation systems, and the ability to manage and solve complex challenges dynamically.

Qualifications

• 8+ years of post-Bachelor’s ML industry experience; or a Master’s degree and 7+ years; or a PhD and 4+ years.
• 1+ years of experience leading machine learning teams focusing on ranking or recommendations.
• Experience with real-time recommendation or search ranking systems is preferred.

Responsibilities

• Lead a team of engineers to build large-scale indexing, retrieval, and ranking systems.
• Collaborate with product teams to define system architecture and vision.
• Build evaluation frameworks for rapid iteration and high-quality decision-making.

Skills

Machine Learning approaches

Large language models

Management and mentorship

Communication skills

Problem-solving

Education

Bachelor’s in a related technical field

Tools

TensorFlow

PyTorch

Spark ML

Job description

Snap Inc. is seeking a seasoned leader to guide a team of machine learning and software engineers in developing large-scale indexing and ranking systems for Snapchat ads. This role involves collaboration with product teams to define architecture and vision while nurturing a high-performing team by upholding standards of engineering and machine learning excellence.

The ideal candidate will have significant industry experience and a strong understanding of machine learning, recommendation systems, and the ability to manage and solve complex challenges dynamically.