Growth ML Engineer: Personalization & Onboarding

Whatnot; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Flexible Time off Policy

Health Insurance

Work From Home Support

Care benefits

Retirement plan and 401k matching

Parental Leave

Job summary

Whatnot is seeking a Machine Learning Engineer to join our Buyer Growth team in San Francisco. In this role, you will lead the design and development of ML models to drive user acquisition and retention. The ideal candidate has over 5 years of experience in building and deploying machine learning systems and expertise in personalization and ranking. You will have the opportunity to directly impact buyer growth and enhance user engagement across our platform. Benefits include flexible time off, health insurance, and a supportive work-from-home policy.

Qualifications

• 5+ years of industry experience building and deploying ML systems at scale.
• Demonstrated success in leading end-to-end ML projects that drove measurable business outcomes.
• Deep expertise in personalization, ranking, or user modeling.

Responsibilities

• Lead the design, development, and productionization of ML models.
• Own the full model lifecycle: from data collection to online experimentation.
• Identify and prioritize high-impact opportunities for machine learning.

Skills

Machine Learning

Python

SQL

Data-driven decision making

Cross-functional collaboration

Tools

PyTorch

TensorFlow

XGBoost

Job description

Whatnot is seeking a Machine Learning Engineer to join our Buyer Growth team in San Francisco. In this role, you will lead the design and development of ML models to drive user acquisition and retention. The ideal candidate has over 5 years of experience in building and deploying machine learning systems and expertise in personalization and ranking. You will have the opportunity to directly impact buyer growth and enhance user engagement across our platform. Benefits include flexible time off, health insurance, and a supportive work-from-home policy.