Machine Learning Engineer, Level 5

Snap Snap · Consumer · Palo Alto, CA +6

Machine Learning Engineer at Snap Inc. responsible for building and deploying ML models for core products, owning the full ML lifecycle, and partnering with cross-functional teams to launch ML-driven features. The role involves applying modern ML techniques to solve large-scale problems and utilizing AI tools for development.

What you'd actually do

  1. Build and deploy machine learning models that power core products, serving millions of Snapchatters
  2. Apply modern ML techniques to solve large-scale, real-world problems
  3. Own the full ML lifecycle from data analysis to production deployment
  4. Partner with cross-functional teams to prototype and launch ML-driven features
  5. Utilize AI tools to design and ship scalable services while upholding rigorous standards for code correctness, security, and production

Skills

Required

  • Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience
  • 5+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 1 years of post-grad machine learning experience
  • Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or other relevant applications of machine learning
  • Proficiency in, or a strong aptitude for, leveraging AI tools to streamline development, paired with the critical judgment to audit generated output for architectural integrity, performance bottlenecks, and security risks

Nice to have

  • Advanced degree in computer science or related field
  • Experience working with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks
  • Experience working with machine learning, ranking infrastructures, and system design

What the JD emphasized

  • critical judgment to audit generated output for architectural integrity, performance bottlenecks, and security risks

Other signals

  • Build and deploy machine learning models that power core products, serving millions of Snapchatters
  • Apply modern ML techniques to solve 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