Grailed logo
Grailed

Grailed is one of the largest online marketplaces to buy & sell authentic luxury, streetwear, vintage, sneakers & more.

Staff Machine Learning Engineer

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteLeadTeam 51-200H1B SponsorCompany SiteLinkedIn

Location

California + 1 moreAll locations: California | New York

Posted

6 days ago

Salary

$159.0K - $233.8K / year

Seniority

Lead

Bachelor Degree7 yrs expEnglishAirflowCloudPythonSQL

Job Description

Staff Machine Learning Engineer

Grailed

• 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

Job 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

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