Support Engineer I

About the Role The ML Production Support Engineer monitors production ML systems to ensure reliable performance, tracking system outputs, data quality input, and overall system behavior. This role triages ML system anomalies, validates datasets used for training and inference, and collaborates with external annotators and vendors to maintain high-quality labeling. The ideal candidate combines strong analytical thinking with hands-on experience in data validation and ML production monitoring.

Key Responsibilities

• Monitor production ML systems to ensure reliable performance, tracking outputs, input data quality, and system behavior.

• Triage ML system anomalies, identify root causes, and drive mitigation actions with engineering and data teams.

• Build and improve QA, monitoring, and investigation workflows for ML systems.

• Validate and assess datasets used for training and inference for completeness and consistency.

• Collaborate with external annotators and vendors to define guidelines, audit labels, and improve labeling quality.

• Contribute to the development of processes and automation that improve data validation and quality pipelines.

• Manage dataset lifecycle, including acquisition, documentation, versioning, and governance.

• Maintain documentation, runbooks, dashboards, and reporting for recurring issues and system health.

Key Skills

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

• Experience working with datasets, including cleaning, validation, and analysis.

• Familiarity with annotation workflows and data labeling processes.

• Experience analyzing production ML system outputs and identifying quality issues.

• Understanding of the ML lifecycle and production monitoring concepts.

• Knowledge of SQL for data querying and analysis, with familiarity with NoSQL databases.

• Strong analytical thinking, attention to detail, and clear communication with technical and non-technical stakeholders.

Good to Have

• Experience with Prometheus, Grafana, and Python (including pytest).

• Familiarity with cloud platforms, preferably Azure.