Machine Learning Engineer, Applied Research

📋 Job Overview

🚀 Join the Future of Commerce with Whatnot!

Whatnot is the largest live shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.

🏢 About Whatnot

As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact. We're one of the fastest growing marketplaces and were recently named the #1 Best Startup Employer in America by Forbes. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business and bring people together through commerce.

🎯 The 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.

✅ Key Responsibilities

• 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

📌 Required Qualifications

• 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 Experience

• Experience in two-sided marketplaces, ads and auction systems, or pricing
• Experience building simulators or economic models of platform behavior

🎁 Benefits

For 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.

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.

🛂 Visa & Eligibility

NYC Based: Team members in this role are required to be within commuting distance (50 miles) of our New York City hub.