Staff Machine Learning Engineer
New
G
GrailedE-commerce Marketplace
Remote USFull-TimeStaff
Salary159,040 - 233,800 USD per year
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Job Details
- Experience
- 7+ years
- Required Skills
- PythonSQLApache AirflowMachine LearningSoftware Engineeringdbt
Requirements
- 7+ years of engineering experience with substantial depth in production machine learning systems.
- Demonstrated end-to-end ownership from training pipelines through deployed inference.
- Advanced knowledge of ML, AI, and statistical models applied to e-commerce settings.
- Strong proficiency in Python, SQL, DBT, and airflow (or similar tools).
- Solid software engineering fundamentals.
- Experience with ranking, retrieval, or recommendation systems.
- Expertise with ML lifecycle tooling (experiment tracking, model versioning, pipeline orchestration, drift detection).
- Experience working with modern data infrastructure including cloud warehouses and search/retrieval systems.
- Ability to evaluate technical approaches against production constraints like latency and reliability.
- Strong communication skills for translating model behavior and tradeoffs to non-technical stakeholders.
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 to ensure accuracy over time.
- Partner with Data Science, Data Engineering, Product Management, and backend engineering to move validated approaches to production systems.
- Own the decision-making process for leveraging GOAT Group ML infrastructure versus building in-house solutions.
- Contribute to ML infrastructure decisions regarding serving architecture, feature computation, and pipeline orchestration.
- Set technical standards and evaluate how ML systems are built and operated across the pod.
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