Hybrid ML Engineer, Marketplace Research & Modeling

Whatnot; New York (NY); Hybrid; USD 207,000 - 290,000; Full time

Benefits offered by this job

Flexible Time Off Policy

Health insurance options (Medical, I.D

Work From Home support

Home office setup allowance

Monthly cell phone & internet stipend

Annual childcare allowance

Lifetime family planning benefit

401k with employer match up to 4%

Job summary

Whatnot in New York City is hiring a Machine Learning Engineer, Applied Research to lead applied research and ML work that turns marketplace questions into models and experiments, moving from hypothesis to production and helping improve how decisions and experimentation are evaluated.

You will build system-level models of marketplace dynamics, including learned simulators and surrogate models, and drive research from hypothesis to production with cross-functional teams in Discovery and Seller.

Qualifications

• 5+ years of industry experience building and deploying ML models at scale.
• Expertise 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 real-world problems using consumer-scale data.
• Advanced proficiency in Python, SQL, 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.

Responsibilities

• 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 marketplace 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.

Skills

5+ years experience

recommendation systems

causal inference

off-policy evaluation

reinforcement learning

auction design

marketplace experimentation

Tools

Python

SQL

PyTorch

XGBoost

Job description

Whatnot in New York City is hiring a Machine Learning Engineer, Applied Research to lead applied research and ML work that turns marketplace questions into models and experiments, moving from hypothesis to production and helping improve how decisions and experimentation are evaluated.

You will build system-level models of marketplace dynamics, including learned simulators and surrogate models, and drive research from hypothesis to production with cross-functional teams in Discovery and Seller.