Lead ML Engineer, Signals & Measurement

SNAP, Inc.; Palo Alto (CA); On-site; USD 235,000 - 414,000; Full time

Benefits offered by this job

Paid parental leave

Comprehensive medical coverage

Mental health support

Job summary

Snap Inc. is seeking a Principal Machine Learning Engineer for the Signals & Measurement team in the United States. You will design and scale ML systems spanning data acquisition to causal measurement and support monetization strategies with advanced ranking models.

You will lead cross‑functional collaboration, apply the latest ML techniques, and shape the ML tech stack while driving performance and cost‑efficient solutions for a global product ecosystem.

Qualifications

• Bachelor’s degree in a technical field such as CS, math, or stats.
• 9+ years of post‑Bachelor ML experience; or a Master’s + 8+ years; or PhD + 5+ years.
• 2+ years of technical leadership or domain expertise.
• Experience developing scalable ML models for recommendation/ranking.
• Experience with TensorFlow, PyTorch, or related frameworks.

Responsibilities

• Design and develop ML systems across the signal lifecycle: acquisition, curation, matching, attribution, and causal measurement.
• Design, implement, and scale ML models to support monetization strategies.
• Collaborate with cross‑functional teams to align ML strategies with company objectives.
• Stay up to date with ML advancements and apply them to solve problems.
• Provide technical direction across the ML community and organization.

Skills

ML leadership

Online ranking

Deep learning

Machine learning

Education

Bachelor's degree

Advanced degree preferred

Tools

TensorFlow

PyTorch

Job description

Snap Inc. is seeking a Principal Machine Learning Engineer for the Signals & Measurement team in the United States. You will design and scale ML systems spanning data acquisition to causal measurement and support monetization strategies with advanced ranking models.

You will lead cross‑functional collaboration, apply the latest ML techniques, and shape the ML tech stack while driving performance and cost‑efficient solutions for a global product ecosystem.