Role- 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
NYC Based:Team members in this role are required to be within commuting distance (50 miles) of our New York City hub.
YouCurious 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
CompensationFor Full-Time (Salary) US-based applicants: $210,000k/year to $300,000k/year + benefits + equity.
The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills, and expertise. This range is only inclusive of base salary, not benefits (more details below) or equity.
Benefits- Flexible Time off Policy and Company-wide Holidays (including a spring and winter break)
- Health Insurance options including Medical, Dental, Vision
- Work From Home Support
- Home office setup allowance
- Monthly allowance for cell phone and internet
- Care benefits
- Monthly allowance 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 + one month gradual return to work *company leave allowances run concurrently with country leave requirements which take precedence.
Please note: Whatnot will only contact you through official [redacted].com email addresses. If you see an email impersonating a Whatnot recruiter, please disregard and report it as spam. 
