AI / ML Platform Engineer

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. We're re-defining e-commerce by blending community, shopping, and entertainment into a community just for you. As a remote co-located team, we're inspired by innovation and anchored in our values. With hubs in the US, UK, Germany, Ireland, and Poland, we're building the future of online marketplaces -together.

From fashion, beauty, and electronics to collectibles like trading cards, comic books, and even live plants, our live auctions have something for everyone.

And we're just getting started! As one of the fastest growing marketplaces, we're looking for bold, forward-thinking problem solvers across all functional areas. 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 builders-intellectually curious, highly entrepreneurial engineers eager to shape the future of AI and ML at Whatnot. You'll design and scale the core infrastructure that powers machine learning and self-hosted large language model applications across the company, working side by side with machine learning scientists to bring cutting-edge models into production and unlock entirely new product experiences. This means building systems that make advanced ML dependable and fast at scale-from low-latency, large model serving to distributed training & high-throughput GPU inference.

What you'll do :

• Own the infrastructure powering AI and ML models across critical business surfaces-supporting growth, recommendations, trust and safety, fraud, seller tooling, and more.
• Prototype, deploy, and productionalize novel ML architectures that directly shape user experience and marketplace dynamics.
• Design and scale inference infrastructure capable of serving large models with low latency and high throughput.
• Build distributed training and inference pipelines leveraging GPUs and both model and data parallelism.
• Stretch beyond your comfort zone to take on new technical challenges as we scale AI across Whatnot's ecosystem.

US Based : 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 New York, Seattle, Los Angeles, and San Francisco hubs.

You

Curious about who thrives at Whatnot? We've found that low ego, a growth mindset, and leaning into action and high impact goes a long way here.

As our next AI / ML Platform Engineer you should have 4+ years of professional experience developing machine learning systems and algorithms, plus :

Compensation

For US-based applicants : $225,000 - $320,000 / year + benefits + stock options

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 in the form of stock options.

Benefits

Home office setup allowance

Monthly allowance for wellness

All Whatnauts are expected to develop a deep understanding of our product. We're passionate about building the best user experience, and all employees are expected to use Whatnot as both a buyer and a seller as part of their job (our dogfooding budget makes this fun and easy!).

16 weeks of paid parental leave + one month gradual return to work

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.