Machine Learning Engineer – Level 3

Jobtailor; California (MO); On-site; USD 120,000 - 180,000; Full time

Job summary

Snapchat is seeking a talented machine learning engineer to build and deploy ML models for core products. You will apply established techniques to solve real-world problems and participate in data analysis, model training, evaluation, and production deployment.

You will collaborate with cross-functional partners to implement ML-driven features and design scalable services. Our team emphasizes code correctness, security, and production-ready quality in a fast-paced environment.

Qualifications

• Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience

Responsibilities

• Build and deploy machine learning models for core Snapchat products
• Apply established machine learning techniques to solve well-defined, real-world problems
• Contribute to the machine learning lifecycle, including data analysis, model training, evaluation, and production deployment
• Collaborate with cross-functional partners and teammates to implement and launch ML-driven features
• Use AI tools and high-velocity engineering workflows to design and ship scalable services
• Uphold rigorous standards for code correctness, security, and production-ready quality

Skills

Machine Learning

Data Analysis

Collaboration

Problem Solving

Independence

Mentorship

Adaptability

Education

Bachelor's Degree in Computer Science

Master's Degree in Relevant Field

Tools

TensorFlow

PyTorch

Scikit-learn

Caffe2

Spark ML

Job description

Build and deploy machine learning models for core Snapchat products
Apply established machine learning techniques to solve well-defined, real-world problems
Contribute to the machine learning lifecycle, including data analysis, model training, evaluation, and production deployment
Collaborate with cross-functional partners and teammates to implement and launch ML-driven features
Use AI tools and high-velocity engineering workflows to design and ship scalable services
Uphold rigorous standards for code correctness, security, and production-ready quality

Requirements

• Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience
• 1+ years of post-Bachelor’s machine learning experience; or Master’s degree in a relevant technical field
• Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or other relevant applications of machine learning
• Strong understanding of machine learning approaches and algorithms
• Ability to prioritize duties and work well independently
• Ability to work with internal and external partners
• Skilled at solving open ambiguous problems
• Strong collaboration and mentorship skills
• Adaptability in learning and applying evolving AI systems and tools
• Advanced degree in computer science or related field (preferred)
• Experience with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks (preferred)
• Experience with machine learning, ranking infrastructures, and system design (preferred)

Core Competencies

Demonstrates expertise in building and deploying machine learning models, with a strong understanding of algorithms and frameworks. Capable of collaborating with cross-functional teams to implement scalable AI-driven solutions while maintaining high standards for code quality and security.

Highest-signal resume keywords

• Machine Learning Model Development
• TensorFlow
• PyTorch
• Data Analysis
• Collaboration Skills

Hard Skills

• Machine Learning
• Model Training
• Model Evaluation
• Ranking Algorithms
• Recommendations Systems
• Content Understanding
• Image Generation
• Machine Learning Approaches
• System Design
• AI Tools

Soft Skills

• Problem Solving
• Adaptability
• Mentorship Skills
• Independence
• Collaboration

Certifications & Qualifications

• Bachelor's Degree in Computer Science
• Master's Degree in Relevant Field

Industry Keywords

• Machine Learning Lifecycle
• Cross-Functional Collaboration
• Production Deployment
• Code Correctness
• Security Standards

Tools & Technologies

• Caffe2
• Spark ML
• Scikit-learn