Senior ML Engineer – Deploy AI at Scale (Equity Eligible)

SNAP, Inc.; New York (NY); On-site; USD 209,000 - 313,000; Full time

Benefits offered by this job

Paid parental leave

Comprehensive medical coverage

Emotional and mental health support

Job summary

Snap Inc. is seeking a Machine Learning Engineer to build and deploy ML models that power core features for millions of Snapchat users.

You will apply state-of-the-art ML techniques to solve large-scale, real-world problems and own the end-to-end ML lifecycle from data analysis to production deployment. You will collaborate with cross-functional teams to prototype and launch ML-driven features, while upholding rigorous standards for code correctness, security, and scalable services.

Qualifications

• Bachelor's degree in CS or related field required.
• 5+ years ML experience; or MS with 4+ years, or PhD with 1+ year.
• Experience building ML models for ranking, recommendations, search or content understanding.

Responsibilities

• Build and deploy ML models powering core products for millions of Snapchatters.
• Apply modern ML techniques to large-scale real-world problems.
• Own the full ML lifecycle from data analysis to production deployment.
• Partner with cross-functional teams to prototype and launch ML-driven features.
• Ensure code correctness, security, and scalable services.

Skills

ML algorithms

Independent work

Cross-functional collaboration

Ambiguity solving

Mentorship

AI tooling

AI system adaptation

Education

Bachelor's degree in CS or related field

Master's degree in technical field

PhD in technical field

Tools

TensorFlow

Caffe2

PyTorch

Spark ML

scikit-learn

Job description

Snap Inc. is seeking a Machine Learning Engineer to build and deploy ML models that power core features for millions of Snapchat users.

You will apply state-of-the-art ML techniques to solve large-scale, real-world problems and own the end-to-end ML lifecycle from data analysis to production deployment. You will collaborate with cross-functional teams to prototype and launch ML-driven features, while upholding rigorous standards for code correctness, security, and scalable services.