Staff Machine Learning Engineer

Job not on LinkedIn

🕒 July 20

🏄 California, New York – Remote

🏄 California – Remote 🗽 New York – Remote

💵 $159k - $233.8k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

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Grailed

WebsiteLinkedInAll Job Openings

51 - 200 employees

🏪 Marketplace

👗 Fashion

🛍️ eCommerce

💰 $60M Series B on 2021-09

Marketplace • Fashion • eCommerce

Grailed is an online marketplace specializing in buying and selling high-end secondhand and new men's and women's fashion. It offers a wide variety of clothing categories including tops, outerwear, footwear, and accessories from leading global designers. Grailed emphasizes authentication and buyer protection, ensuring a secure shopping experience. The platform also features resources for exploring fashion collections and discovering new styles.

📋 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 off • dental • medical • vision • disability • life insurance options