Principal ML Engineer — Trust & Safety Platforms

AI Pulse; Bellevue (WA); On-site; USD 262,000 - 414,000; Full time

Benefits offered by this job

Equity (RSUs)

Competitive compensation packages

Job summary

Snap Inc. is seeking a Principal Machine Learning Engineer to join the Platform Integrity Engineering team. You will design, implement, and scale ML models that protect Snapchatters and uphold a healthy content ecosystem, collaborating with cross-functional teams to shape ML strategy and technology choices.

You will lead and mentor a team of engineers, stay at the forefront of ML advances, and ensure performance, availability, and cost efficiency across the ML stack in a fast-paced environment.

Qualifications

• Experience leading ML teams delivering scalable models.
• Proven ability to design and ship real-time ML systems.
• Strong collaboration across product and engineering.

Responsibilities

• Design, implement, and scale ML models for trust and safety and content moderation.
• Collaborate with cross-functional teams to set ML strategy and roadmaps.
• Stay updated on ML advances and apply them to Snap's platforms.
• Lead and mentor engineers to raise the ML capability of the team.
• Ensure reliability, performance, and cost efficiency of ML systems.

Skills

Machine learning

Deep learning

Computer vision

Natural language processing

LLMs/VLMs

Leadership

Mentorship

Collaboration

Proactive learning

Education

Bachelor's degree in a technical field

Master's degree in a technical field

PhD in a related technical field

Tools

TensorFlow

PyTorch

Deep learning frameworks

Job description

Snap Inc. is seeking a Principal Machine Learning Engineer to join the Platform Integrity Engineering team. You will design, implement, and scale ML models that protect Snapchatters and uphold a healthy content ecosystem, collaborating with cross-functional teams to shape ML strategy and technology choices.

You will lead and mentor a team of engineers, stay at the forefront of ML advances, and ensure performance, availability, and cost efficiency across the ML stack in a fast-paced environment.