Snap
Machine Learning Engineer, Level 5
$178K–$313K
Palo Alto, California +4 · Full-time · Posted Oct 1, 2026
- Location
- Palo Alto, California
San Francisco, California
Los Angeles, California
New York, New York
Bellevue, Washington - Experience
- 5+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 4+ years of post-grad machine learning experience; or PhD in a relevant technical field + 1 year of post-grad machine learning experience.
- Education
- Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience
About the job
Builds and deploys machine learning models and services for Snap products, owning the ML lifecycle and partnering with teams to launch ML-driven features.
What you'll do
- Build and deploy models for large-scale products and real-world problems.
- Own the ML lifecycle from data analysis through production deployment.
- Partner with cross-functional teams to prototype and launch ML-driven features, using AI tools to develop scalable services.
What they ask for
- Strong understanding of machine learning approaches and algorithms.
- Experience developing ML models for ranking, recommendations, search, content understanding, image generation, or other relevant applications.
- Bachelor's degree in a relevant technical field or equivalent years of practical work experience.
- 5+ years of post-Bachelor’s ML experience; or a Master’s degree in a technical field plus 4+ years of post-grad ML experience; or a PhD in a relevant technical field plus 1 year of post-grad ML experience.
Nice to have
- Advanced degree in computer science or a related field.
- Experience with machine learning frameworks, ranking infrastructure, or system design.
Pay
$209,000-$313,000 annually (USD, base salary); $199,000-$297,000 annually (USD, base salary); $178,000-$266,000 annually (USD, base salary)
Tools
TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn
AI focus
Develops and deploys machine learning models for products and ML-driven features; uses AI tools to design and ship scalable services and audit generated output for architecture, performance, and security.
Model-written summary · the original posting is authoritative