**Who this is for** This role is for experienced software engineers who are passionate about building the foundational infrastructure that powers large-scale ma
Work type: onsite
Location: San Francisco, CA | Los Angeles, CA | New York, NY | Seattle, WA
Salary: $190,000 – $300,000/yr
Type: Full-time
**Who this is for** This role is for experienced software engineers who are passionate about building the foundational infrastructure that powers large-scale machine learning and generative AI applications within a fast-paced, high-growth marketplace. **Key highlights** You will work at the intersection of infrastructure and data science to design and scale low-latency model serving, distributed training pipelines, and GPU inference systems that directly impact user experiences ranging from recommendations to fraud detection. **You might be a good fit if you...** - Have 4+ years of professional experience in ML systems, including 3+ years of building production-grade software for consumer-scale environments. - Possess strong proficiency in Python and experience managing distributed systems at scale. - Have practical experience with modern observability tools like Datadog or Grafana and operational database systems. - Are an autonomous builder who thrives in a collaborative, cross-functional environment where you can drive technical initiatives from prototype to production.
## 🚀 Join the Future of Commerce with Whatnot!
Whatnot is the largest livestream 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](https://www.whatnot.com/careers) 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](https://www.nytimes.com/2025/10/28/business/dealbook/whatnot-livestream-shopping-fundraise.html) and were recently named the [#1 Best Startup Employer in America](https://www.forbes.com/lists/americas-best-startup-employers/) by Forbes. Check out the latest Whatnot updates on our [news](https://blog.teamwhatnot.com/) and [engineering blogs](https://medium.com/whatnot-engineering) 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:
## 👋 You
People who do well at Whatnot tend to be comfortable figuring things out as they go, biased toward action, and genuinely curious about what they're building. They care more about outcomes than credit and stay close to the product and the people using it.
As our next AI/ML Platform Engineer you should have 4+ years of professional experience developing machine learning systems and algorithms, plus:
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.