Machine Learning Engineer, Applied Research
Job Description
Hybrid in New York, NY (within commuting distance of the NYC hub), this role leads applied research and machine learning work that turns marketplace hypotheses into production systems. You will help improve how experiments are evaluated and how marketplace decisions are made, supporting better simulation, allocation, and long-term outcome modeling across a multi-sided platform.
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.
- Move ideas from hypothesis to production by running literature reviews, building prototypes, performing offline validation, conducting shadow testing, and shipping online experiments through partner teams in Discovery and Seller.
- Build system-level models of how the marketplace behaves, including learned simulators to predict segment-level effects of ranking and policy changes and surrogate models for long-term marketplace outcomes.
- Model Whatnot’s real market mechanics, such as auction and bidding dynamics and discovery exposure allocation treated as a portfolio problem, including allocation to rising sellers.
- Advance evaluation of changes in a live multi-sided marketplace using off-policy evaluation, switchback and interference-robust experiment designs, and variance reduction.
- Strengthen Whatnot’s external technical presence through publications, open-source contributions, and public benchmarks.
What you bring
- 5+ years of industry experience building and deploying ML models to solve user problems at scale.
- Deep experience in at least one area: recommendation systems, causal inference, off-policy evaluation, reinforcement learning and bandits, auction or mechanism design, or marketplace experimentation.
- A demonstrated 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, 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
Compensation
$207K - $290K per year (full-time salary), plus benefits and equity for eligible US-based applicants: $207,000/year to $290,000/year + benefits + equity.
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 home office setup allowance.
- Monthly allowances for cell phone and internet and 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.
Location
New York, NY (Hybrid). Team members in this role are required to be within commuting distance (50 miles) of the New York City hub.
Security note: Whatnot will only contact you through official @whatnot.com email addresses. If you see an email impersonating a Whatnot recruiter, disregard and report it as spam.
EOE: Whatnot is an Equal Opportunity Employer.
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