Staff Machine Learning Engineer

GOAT Group

4d•$159,040 - $233,800

About The Position

Grailed is looking for a Staff Machine Learning Engineer to help build the models and systems that connect buyers to the inventory they're looking for and surface things they didn't know they wanted. The data sits at the center of a complex peer-to-peer marketplace, and the ML layer 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.

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).

Nice To Haves

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

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