Machine Learning Engineer, Applied Research

Lead applied machine learning research that improves live marketplace dynamics at Whatnot, from hypothesis-driven studies through production experimentation.

Responsibilities

• Own applied research across marketplace dynamics, including simulation, auction and allocation mechanics, long-term objective modeling, exploration and information value, and marketplace experimentation methods
• Drive ideas from hypothesis to production using literature review, prototyping, offline validation, shadow testing, and online experiments launched through partner teams in Discovery and the Seller org
• Build system-level models of marketplace behavior, including learned simulators to estimate segment-level effects of ranking and policy changes, plus surrogate models for long-term marketplace outcomes
• Model Whatnot’s real market mechanics, covering auction and bidding dynamics and discovery exposure allocation treated as a portfolio problem, including allocation to rising sellers
• Improve evaluation for changes in a multi-sided live marketplace using off-policy evaluation, switchback and interference-robust experiment designs, and variance reduction techniques
• Strengthen Whatnot’s external technical presence via publications, open-source contributions, and public benchmarks

Requirements

• 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
• Proven track record applying scientific methods to real-world problems on 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 influencing roadmaps and aligning cross-functional teams in a remote environment
• Experience with two-sided marketplaces, ads and auction systems, or pricing
• Experience building simulators or economic models of platform behavior

Technologies

• Python
• SQL
• PyTorch
• XGBoost

Compensation

• Full-Time (Salary) US-based applicants: $210,000/year to $300,000/year + benefits + equity
• Final salary depends on level, relevant experience, skills, and expertise; the listed range covers base salary only
• Compensation Range: $210K - $300K

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

Location: Seattle, WA (remote)

Summary: Lead applied ML research for live marketplace dynamics at Whatnot, moving from hypothesis to production and experimentation. Build models and methods for simulation, auction and allocation mechanics, multi-sided evaluation, and contribute to external technical presence.

Please 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. They value diversity and do not discriminate on protected characteristics under local law.