LLM 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.

We’re looking for builders–intellectually curious, highly entrepreneurial engineers eager to shape the future of AI and ML ’ll design and scale the core infrastructure that powers 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 AI dependable and fast at scale–from building retrieval systems to more effectively ground LLM responses in Whatnot’s business context to developing scalable LLM evaluation frameworks and human‑in‑the‑loop feedback mechanisms.

• Own the infrastructure powering LLMs across critical business surfaces– supporting growth, recommendations, trust and safety, fraud, seller tooling, and more.
• Create robust and scalable LLM evaluation frameworks to measure model performance, guide iteration, and prevent regression via CI/CD.
• Deploy RAG systems and MCP servers to more effectively ground LLM responses in Whatnot’s business context while enforcing rigorous PII controls.
• Design efficient human‑in‑the‑loop feedback pipelines that can be used to inform scalable LLM evaluation.
• Bridge the gap between research and production, helping to transform experimental ideas into scalable solutions.
• 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.

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 Engineer you should have 4+ years of professional experience developing machine learning systems and algorithms, plus:

• Bachelor’s degree in Computer Science, Statistics, Applied Mathematics or a related technical field, or equivalent work experience.
• 3+ years of software engineering experience building and maintaining production systems for consumer‑scale loads.
• 1+ years of professional experience developing software in Python.
• Ability to work autonomously and drive initiatives across multiple product areas and communicate findings with leadership and product teams.
• Experience with operational, search, and key‑value databases such as Postgre
  
  SQL, Dynamo
  
  DB, Elasticsearch, Redis.
• Firm grasp of visualization tools for monitoring and logging e.g., Data Dog, Grafana.
• Familiarity with cloud computing platforms and managed services such as AWS Sagemaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Apache Kafka, Flink.
• Professionalism around collaborating in a remote working environment and well tested, reproducible work.
• Exceptional documentation and communication skills.

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

Thesalaryrangemaybeinclusiveofseverallevelsthatwouldbeapplicabletotheposition.

Finalsalarywillbebasedonanumberoffactorsincluding,level,relevantpriorexperience,skills,…