Machine Learning Engineer, Applied Research

Whatnot

. Lead research projects across marketplace dynamics, including simulation, auction and allocation mechanics, long-term objective modeling, exploration and information value, and marketplace experimentation methods .

Posted 9/17/2026full-timeNew York City • New York • United StatesMid-LevelSenior💰 $210,000 - $300,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and deploying machine learning models, particularly in recommendation systems, causal inference, and auction dynamics. Proficient in advanced statistical methods and experiment design, with a strong ability to collaborate and influence cross-functional teams.

Highest-signal resume keywords

Machine Learning Model DeploymentRecommendation SystemsCausal InferencePython ProficiencyApplied Statistics

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningExperiment DesignCausal InferenceReinforcement LearningAuction DesignMarketplace ExperimentationSimulators BuildingStatistical AnalysisData AnalysisModeling

Soft Skills

Strong CommunicationLeadership SkillsCross-Functional CollaborationInfluencing RoadmapsRemote Team Alignment

Tools & Technologies

PythonSQLPyTorchXGBoostML Frameworks

Industry Keywords

Marketplace DynamicsTwo-Sided MarketplacesAds SystemsAuction SystemsEconomic Models

Tech Stack

Tools & technologies

PythonPyTorchSQL

About the role

Key responsibilities & impact

Lead research projects across marketplace dynamics, including simulation, auction and allocation mechanics, long-term objective modeling, exploration and information value, and marketplace experimentation methods
Take ideas from hypothesis to production through literature review, prototyping, offline validation, shadow testing, and online experiments shipped through partner teams in Discovery and the Seller organization
Build learned simulators predicting segment-level effects of ranking and policy changes
Build surrogate models of long-term marketplace outcomes
Model auction and bidding dynamics and discovery exposure allocation as a portfolio problem, including allocation to rising sellers
Advance marketplace evaluation through off-policy evaluation, switchback and interference-robust experiment designs, and variance reduction
Contribute to external technical presence through publications, open-source work, and public benchmarks
Collaborate with cross-functional teams and influence roadmaps

Requirements

What you’ll need

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
Experience in two-sided marketplaces, ads and auction systems, or pricing (preferred)
Experience building simulators or economic models of platform behavior (preferred)
Must be within commuting distance (50 miles) of the New York City hub
New hires participate in an in-person onboarding experience at one of the offices

Benefits

Comp & perks

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