Software Engineer, Core Machine Learning

About the role

🚀 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

We're looking for entrepreneurial software engineers who thrive on ambiguous, 0-to-1 work. You’ll design and deploy both traditional ML models and infrastructure (contextual bandits to manage ad spend, shipment ETA predictions) and LLM-powered systems (RAG pipelines, human-in-the-loop evaluation frameworks) to address the business’s most pressing problems. Your job will change every ~12 months as we hand off stable products and pivot to address the business’s next big need. Strong applicants display exceptional business judgement and are equally comfortable contributing to production infrastructure and training the models that run on it. They build for the long term while finding ways to aggressively pull forward business impact.

What you'll do:

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Develop and deploy new both production machine learning algorithms and core infrastructure to enrich the buyer and seller experience.

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Contribute to and set direction across the entire machine learning development process: opportunity sizing, feature engineering, model training, evaluation, load testing, deployment, and alerting.

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Design and implement supporting software infrastructure to unblock your work: streaming pipelines, async worker queues, event enrichment infrastructure, continuous training loops, model monitoring systems, LLM tool access controls, and dynamic agent evaluation framework to give just a few examples

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Define project roadmaps that deliver value quickly while building towards scalable, extensible long-term systems

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Bridge research and production: take an experimental idea and harden it into a scalable, well-tested, monitored system.

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Communicate tradeoffs and findings clearly to product and leadership.

We offer flexibility to work from home or from one of our global office hubs, and we value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our Seattle or San Francisco hub.