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# Machine Learning Engineer, Applied Research

- **Company:** Whatnot Inc.
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Expert
- **Salary:** $210,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Machine Learning, Open Source Technology, Recommender Systems, Tensorflow, SQL Databases, Reinforcement Learning, Pytorch, Xgboost, Machine Learning Operations
- **Published:** September 16, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=cf7b9a272e2ba1dc

## About the Role

Curious about who thrives at Whatnot? We've found that embodying a low ego, growth mindset, and high-impact drive goes a long way here. As our next Machine Learning Engineer you should have:

 * 5+ years of industry experience building and deploying ML models to solve user problems at scale
 * Depth in at least one of: recommendation systems, causal inference, off-policy evaluation, reinforcement learning and bandits, auction or mechanism design, or marketplace experimentation
 * A track record of applying scientific methods to solve real-world problems on consumer-scale data
 * Advanced proficiency in Python, SQL, and common ML frameworks like PyTorch, XGBoost, etc
 * Strong grounding in applied statistics, experiment design and theoretical machine learning
 * Strong communication and leadership skills; ability to influence roadmaps and align cross-functional teams in a remote environment
 * Preferred Qualifications:
 + Experience in two-sided marketplaces, ads and auction systems, or pricing
 + Experience building simulators or economic models of platform behavior

## Description

* Lead research projects across the marketplace dynamics problem area: simulation, auction and allocation mechanics, long-term objective modeling, exploration and information value, or marketplace experimentation methods
 * Take ideas from hypothesis to production: literature review, prototyping, offline validation, shadow testing, and online experiments shipped through partner teams in Discovery and the Seller org
 * Build models of how the marketplace behaves as a system: learned simulators that predict segment-level effects of ranking and policy changes, and surrogate models of long-term marketplace outcomes
 * Model Whatnot's actual market mechanics: auction and bidding dynamics, and discovery exposure allocation as a portfolio problem, including allocation to rising sellers
 * Advance how a multi-sided live marketplace evaluates changes: off-policy evaluation, switchback and interference-robust experiment designs, and variance reduction
 * Contribute to Whatnot's external technical presence through publications, open-source work, and public benchmarks