Staff Machine Learning Engineer

ROLE OVERVIEW Grailed is looking for a Staff Machine Learning Engineer to help us build the models and systems that connect buyers to the inventory they're looking for — and surface things they didn't know they wanted. Our data sits at the center of a complex peer-to-peer marketplace, and the ML layer is what turns a decade of behavioral signals into better search, smarter recommendations, and a marketplace that gets sharper over time. This is a hands-on technical role for an engineer who takes end-to-end ownership seriously — from architecture through production operation — and who is energized by working on a small, focused team where the infrastructure is still being built and the decisions made now have lasting consequences. The strongest candidates will bring production instincts alongside technical depth: the kind of engineer who isn't done when the model trains, and who treats monitoring, retraining, and reliability as part of the job, not a follow-on task. What You'll Do Own the full lifecycle of predictive models in production — architecture, training pipelines, inference infrastructure, deployment, and ongoing model health Build and operate the systems that route model outputs into live product surfaces: search ranking, recommendations, feed ordering, and related user-facing experiences Establish and maintain model monitoring, alerting, drift detection, and retraining cadences — the feedback loops that keep deployed models accurate over time Partner closely with Data Science, Data Engineering, Product Management, and backend engineering to move work from validated approach to production system Own the decision-making process on whether to leverage ML infrastructure & expertise from our parent company, GOAT Group, and when to advocate for building in-house solutions. Contribute to ML infrastructure decisions — serving architecture, feature computation, pipeline orchestration — with an eye toward what scales as the team and model count grows Set technical standards and raise the bar for how ML systems are built, evaluated, and operated across the pod Technical Requirements 7+ years of engineering experience, with substantial depth in production machine learning systems. Demonstrated end-to-end ownership: training pipelines through deployed inference, not just modeling. Advanced knowledge of ML, AI and statistical models, as well their application in e-commerce settings.   Strong proficiency in Python; SQL; DBT; airflow or similar. Solid software engineering fundamentals. Experience with ranking, retrieval, or recommendation systems. Demonstrated expertise with ML lifecycle tooling — experiment tracking, model versioning, pipeline orchestration, drift detection — and comfort working with modern data infrastructure (cloud warehouse, search/retrieval systems). What We're Looking For Takes ownership of developing repeatable end-to-end processes, not just outcomes Evaluates technical approaches against production constraints —...