Staff Machine Learning Engineer

Billy Goat Group

Staff Machine Learning Engineer at Grailed building and operating ML models and systems. Collaborate across teams to establish model monitoring and maintain accurate deployed models.

Posted 7/17/2026full-timeRemote • 🇺🇸 United StatesLead💰 $159,040 - $233,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in managing the full lifecycle of predictive models, including architecture, training pipelines, and deployment, while ensuring model accuracy through monitoring and retraining. Proficient in collaborating with cross-functional teams to integrate machine learning solutions into e-commerce applications.

Highest-signal resume keywords

End-To-End Ownership Of ML SystemsAdvanced Knowledge Of ML And AIStrong Proficiency In Python And SQLExperience With Ranking And Recommendation SystemsExpertise In ML Lifecycle Tooling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningArtificial IntelligenceStatistical ModelsTraining PipelinesModel DeploymentModel MonitoringDrift DetectionFeature ComputationPipeline OrchestrationSoftware Engineering Fundamentals

Tools & Technologies

PythonSQLDBTAirflowCloud WarehouseSearch SystemsRetrieval Systems

Industry Keywords

E-CommercePredictive ModelsModel VersioningExperiment TrackingML Infrastructure

Tech Stack

Tools & technologies

AirflowCloudPythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

401K
paid time off
dental
medical
vision
disability
life insurance options