Machine Learning Engineer, Applied Research

Whatnot is hiring a Machine Learning Engineer, Applied Research for a hybrid role based in New York City (within commuting distance, up to 50 miles from the hub). You will lead applied research and machine learning work that turns marketplace questions into models and experiments, moving from hypothesis to production and helping improve how decisions and experimentation are evaluated.

What you’ll do

• Lead applied research projects focused on marketplace dynamics, including simulation, auction and allocation mechanics, long-term objective modeling, exploration and information value, and marketplace experimentation methods.
• Drive work from hypothesis to production through literature review, prototyping, offline validation, shadow testing, and shipping online experiments through partner teams in Discovery and Seller.
• Build system-level models of how the marketplace behaves, such as learned simulators that estimate segment-level impacts from ranking and policy changes, plus surrogate models for long-term marketplace outcomes.
• Model real market mechanics, including auction and bidding dynamics and discovery exposure allocation as a portfolio problem, with allocation to rising sellers.
• Improve how a multi-sided live marketplace evaluates changes using off-policy evaluation, switchback and interference-robust experiment designs, and variance reduction.
• Help strengthen Whatnot’s external presence through publications, open-source work, and public benchmarks.

What you bring

• 5+ years of industry experience building and deploying ML models to solve user problems at scale.
• Deep expertise in at least one area: 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 real-world problems using consumer-scale data.
• Advanced proficiency in Python, SQL, and common ML frameworks such as PyTorch and XGBoost.
• Strong grounding in applied statistics, experiment design, and theoretical machine learning.
• Strong communication and leadership skills, including the ability to influence roadmaps and align cross-functional teams in a remote environment.
• Preferred: experience in two-sided marketplaces, ads and auction systems, or pricing.
• Preferred: experience building simulators or economic models of platform behavior.

Tools you’ll use

• Python, SQL, PyTorch, XGBoost

Flexible benefits and support

• Flexible Time Off Policy and company-wide holidays, including spring and winter break.
• Health insurance options: Medical, Dental, Vision.
• Work From Home support and a home office setup allowance.
• Monthly allowances for cell phone and internet and for wellness.
• Annual allowance towards Childcare.
• Lifetime benefit for family planning, such as adoption or fertility expenses.
• Retirement: 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally.
• Monthly allowance to dogfood the app.
• Parental Leave: 16 weeks of paid parental leave plus one month gradual return to work.

Compensation

$207,000 to $290,000 per year. For full-time, salary-based US applicants: $207,000/year to $290,000 plus benefits and equity.

Note: Whatnot will only contact candidates through official @whatnot.com email addresses. If you receive an email impersonating a Whatnot recruiter, disregard it and report it as spam.

EOE: Whatnot is an Equal Opportunity Employer.