Machine Learning Engineer, Whatnot

• 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
• 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

Desirable

• 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