Note: The job is a remote job and is open to candidates in USA. Grailed is a leading community-driven marketplace for rare luxury and vintage fashion, and they are seeking a Staff Machine Learning Engineer to enhance their data-driven systems. The role involves end-to-end ownership of predictive models, collaborating with various teams to optimize user experiences through machine learning solutions.
Responsibilities
- 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
Skills
- 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)
- Experience with semantic enrichment, NLP, or multi-modal ML in a production context
- Genuine curiosity about the domain — fashion, resale, style — and the specific ML problems it creates
Benefits
- 401K
- Paid time off
- Dental
- Medical
- Vision
- Disability
- Life insurance options
Company Overview
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