Whatnot - Machine Learning Engineer

• This is a high-impact role where you'll work with other Senior ICs to drive the technical direction, mentor exceptional engineers, and work cross-functionally to build the next generation of ads products. • Team members in this role must live within commuting distance of our London, UK hub. • Lead the design and evolution of machine learning models that power ads retrieval, ranking, and auction systems at scale. • Own end to end ML systems, including training pipelines, feature infrastructure, and low latency online inference for real time and batch use cases. • Define experimentation strategies, success metrics, and evaluation frameworks, and drive iteration through rigorous offline and online testing. • Establish model and system observability through metrics, dashboards, and reliability best practices. • Provide technical leadership through mentorship, design reviews, and raising engineering standards across the Ads+Promos org and Engineering at Whatnot. • Stay current on advances in machine learning and ads auction systems, and drive adoption where they deliver clear impact. • Beyond strong alignment with Whatnot’s cultural values, you’ll bring: • Advanced degree and experience: M.S. or Ph.D. in Computer Science, Machine Learning, Economics, Statistics, or equivalent professional experience, plus 8+ years building production software and ML pipelines/systems, including technical leadership (project/tech lead or equivalent). • Applied ML domain expertise: Experience applying statistical and machine learning methods in at least one of: ads, search, recommendations, content understanding, NLP, or large language models. • Core engineering & ML stack: Strong proficiency in Python and at least one major ML stack (scikit‑learn, PyTorch, LightGBM, etc.), with solid software engineering fundamentals and backend skills; capable of building scalable systems (our stack primarily includes Python, Elixir, and JavaScript) and deploying ML models to production (batch and/or real‑time). • Data platform & features: Experience with data orchestration frameworks (e.g., Dagster, Kubeflow) and feature store design, including end‑to‑end ownership of data and ML pipelines. • Analytics & marketplace insight: Strong data analysis skills; capable of deep behavioral analysis to uncover trends and insights within a complex advertising marketplace. • Product sense & collaboration: Excellent product instincts—you think first about users and business impact, can translate product needs into measurable ML solutions, and collaborate effectively with product, data science, engineering, and other cross‑functional partners. • Teamwork & leadership: A highly collaborative mindset, especially with other senior ML ICs and cross‑functional ML counterparts, to drive measurable business outcomes. • Bonus: experience in formal or informal leadership roles (e.g., Technical Lead or Manager) and a track record of raising the bar for engineering excellence.