Machine Learning Engineer, Growth

Whatnot; Los Angeles (CA); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Flexible time off

Health coverage

Work from home

Home office setup

Cell/internet stipend

Care benefits

Wellness stipend

Childcare allowance

Parental leave

Job summary

Whatnot is expanding its Buyer Growth team with a Machine Learning Engineer who will own models that power onboarding and targeted experiences to accelerate buyer growth and improve first-time conversions. Based in Los Angeles with remote options, this role centers on end-to-end ML initiatives that impact acquisition, activation, and retention for first-time buyers.

Responsibilities include designing and productionizing models for acquisition, activation, and retention, owning the ML lifecycle,

Qualifications

• 5+ years of industry experience building and deploying ML systems at scale.
• Proven track record leading end-to-end ML projects with measurable outcomes.
• Deep expertise in personalization, ranking, or user modeling.
• Strong Python/SQL and ML framework proficiency.
• Ability to influence roadmaps and collaborate cross-functionally.

Responsibilities

• Design, develop, and productionize ML models for onboarding and retention.
• Own the full ML lifecycle: data pipelines, feature engineering, model training, deployment, and online experimentation.
• Collaborate with product, engineering, data, and marketing stakeholders to identify opportunities.
• Drive ML best practices across the Buyer Growth team.

Skills

ML system design

End-to-end ML projects

Personalization/ranking

Communication

Leadership

Tools

Python

SQL

PyTorch

TensorFlow

XGBoost

Job description

Whatnot is expanding its Buyer Growth team with a Machine Learning Engineer who will own models that power onboarding and targeted experiences to accelerate buyer growth and improve first-time conversions. Based in Los Angeles with remote options, this role centers on end-to-end ML initiatives that impact acquisition, activation, and retention for first-time buyers.

Responsibilities

• Design, develop, and productionize ML models that drive user acquisition, activation, and retention, including ranking models, targeting systems, and uplift modeling.
• Own the full ML lifecycle: data pipelines, feature engineering, model training, deployment, and online experimentation; build feedback loops from postbacks, conversions, and downstream revenue signals.
• Identify and prioritize high-impact opportunities where machine learning can unlock growth, collaborating with product, engineering, data, and marketing stakeholders.
• Drive technical excellence and establish ML best practices across the Buyer Growth team.

Requirements

• 5+ years of industry experience building and deploying ML systems at scale.
• Proven track record leading end-to-end ML projects that delivered measurable business outcomes.
• Deep expertise in personalization, ranking, or user modeling, with a strong intuition about consumer behavior in digital products.
• Advanced proficiency in Python, SQL, and common ML frameworks such as PyTorch, TensorFlow, and XGBoost.
• Strong communication and leadership abilities with the capacity to influence roadmaps and align cross-functional teams.

Technologies

• Python
• SQL
• PyTorch
• TensorFlow
• XGBoost

Benefits

• Flexible time off and company-wide holidays, including spring and winter breaks
• Health coverage options (Medical, Dental, Vision)
• Work from home support
• Home office setup allowance
• Monthly stipend for cell phone and internet
• Care benefits
• Wellness stipend
• Annual childcare allowance
• Lifetime support for family planning, including adoption or fertility expenses
• Retirement options including Traditional and Roth 401(k) in the US with employer matching up to 4%, plus pension plans internationally
• Monthly allowance to explore and test the app within the platform
• Dogfood-friendly culture with a budget to support using Whatnot as both buyer and seller
• Parental leave
• 16 weeks of paid parental leave plus a one-month gradual return to work, with country leave requirements taking precedence