Machine Learning Engineer, Applied Research

Whatnot Inc.; Northern, New York (KY, NY); Hybrid; USD 210,000 - 300,000; Full time

Benefits offered by this job

Flexible Time off Policy

Health Insurance options

Work From Home Support

Home office setup allowance

Monthly cell phone/internet allowance

Wellness monthly allowance

Childcare allowance

Lifetime family planning benefit

401k with employer match (US)

Job summary

Whatnot Inc. is seeking an experienced Machine Learning Engineer to lead research on marketplace dynamics, including simulation, auction mechanics, and long-term objective modeling.

You will transform hypotheses into production, build simulators, and develop models predicting ranking and policy effects for a multi-sided live marketplace. You will work with remote teams, contribute to publications and open-source projects, and influence cross-functional roadmaps while advancing experimental

Qualifications

• 5+ years of industry experience building and deploying ML models at scale.
• Depth in at least one area: recommendation systems, causal inference, off-policy evaluation, RL and bandits, auction/mechanism design, or marketplace experimentation.
• Track record applying scientific methods to real-world consumer data.
• Advanced proficiency in Python, SQL, and ML frameworks (PyTorch, XGBoost, etc).
• Strong grounding in applied statistics, experiment design and theoretical ML.
• Strong communication and leadership skills, ability to influence roadmaps across remote teams.

Responsibilities

• Lead research projects across marketplace dynamics: 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 with partner teams.
• Build models of marketplace behavior: learned simulators predicting segment-level effects of ranking and policy changes.
• Model Whatnot's market mechanics: auction/bidding dynamics and discovery exposure as a portfolio problem.
• Advance evaluation techniques: off-policy evaluation, switchback/interference-robust designs, variance reduction.
• Contribute to Whatnot's external technical presence via publications and open-source work.

Skills

5+ years experience

ML model deployment

Python

SQL

Statistical methods

Communication

Leadership

Tools

PyTorch

XGBoost

Job description

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.

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.

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

NYC Based:

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

You

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. As our next Machine Learning Engineer you should have:

• 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

• Preferred Qualifications:

  • Experience in two-sided marketplaces, ads and auction systems, or pricing

  • Experience building simulators or economic models of platform behavior

Compensation

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.

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 *company leave allowances run concurrently with country leave requirements which take precedence.

Please note: Whatnot will only contact you through official @whatnot.com email addresses. If you see an email impersonating a Whatnot recruiter, please disregard and report it as spam.

EOE

Whatnot is proud to be an Equal Opportunity Employer. We value diversity, and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability status, or any other status protected by local law. We believe that our work is better and our company culture is improved when we encourage, support, and respect the different skills and experiences represented within our workforce.