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# Machine Learning Engineer, Growth

- **Company:** Whatnot Inc.
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Expert
- **Salary:** $190,000.0 - $280,000.0
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, SQL Databases, Feature Engineering, Pytorch, Data Analytics, Xgboost, Machine Learning Operations, Data Pipelines
- **Published:** July 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=df0d821e73f5bcd5

## About the Role

Curious about who thrives at Whatnot? We've found that low ego, a growth mindset, and leaning into action and high impact goes a long way here.

 * 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; strong intuition around user behavior in consumer products.
 * Advanced proficiency in Python, SQL, and common ML frameworks (e.g., PyTorch, TensorFlow, XGBoost).
 * Strong communication and leadership skills; ability to influence roadmap and align cross-functional teams.
 * Preferred Qualifications:
 + Experience building ML applications in E-commerce or Social products
 + Prior experience in growth domains: user segmentation, retention modeling, large scale recommendation systems, notification ranking, etc.

## Description

The Buyer Growth team is responsible for driving Whatnot's top-of-funnel growth and first-time buyer success. We're looking for a Machine Learning Engineer to accelerate buyer growth at Whatnot through intelligent, data-driven experiences. As the first ML engineer, you'll build and own ML models personalization in onboarding and user targeting in our acquisition and engagement channels. This is a high-impact role on a team directly responsible for growing Whatnot's buyer base and improving first-time user conversion., * Lead the design, development, and productionization of ML models that power user acquisition, activation, and retention (e.g., ranking models, targeting systems, uplift modeling).

 * Own the full ML lifecycle. Build and own 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.


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