Job Description:
• 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
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).
Benefits:
• 401K
• paid time offdentalmedicalvisiondisabilitylife insurance options