Snap
Machine Learning Engineer, Level 4
$147K–$259K
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
- 3+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 2+ year of post-grad machine learning experience; or PhD in a relevant technical field.
- Education
- Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience.
About the job
Build and deploy machine learning models and scalable services for Snap products, owning the ML lifecycle from data analysis through production and collaborating on ML-driven features.
What you'll do
- Build and deploy machine learning models for core products and large-scale problems.
- Own the ML lifecycle from data analysis to production deployment.
- Partner with cross-functional teams to prototype and launch ML-driven features.
What they ask for
- Strong understanding of machine learning approaches and algorithms.
- Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or other relevant applications.
- Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience.
- Proficiency in, or a strong aptitude for, leveraging AI tools and auditing generated output for architectural integrity, performance, and security.
Nice to have
- Advanced degree in computer science or related field.
- Experience with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, or scikit-learn, and with ML/ranking infrastructure and system design.
Pay
$173,000-$259,000 annually USD base salary; $164,000-$246,000 annually USD base salary; $147,000-$220,000 annually USD base salary
Tools
TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn
AI focus
Build ML models for product applications including ranking, recommendations, search, content understanding, and image generation; use AI tools to streamline engineering and audit generated output.
Model-written summary · the original posting is authoritative