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Manager, Pilot Team

Posted June 16, 2026
Contract Mid-Senior Level

Job Overview

Client Partnership & Solutioning

  • Support client discovery sessions to understand dataset needs, workflow rules, and ML objectives.
  • Translate client requirements into workflows, annotation guidelines, taxonomies, and quality frameworks.
  • Provide clients with delivery updates, performance insights, and workflow recommendations.
  • Support solution scoping, demos, proposal inputs, and client clarifications.
  • Identify opportunities to improve or expand existing programs.

Program Delivery Management

  • Oversee daily execution of assigned AI/ML, Computer Vision, AV/ADAS, and related data workflows.
  • Ensure delivery against agreed KPIs, SLAs, accuracy, throughput, quality, and timelines.
  • Manage onboarding, calibration, guideline updates, workflow transitions, and production readiness.
  • Work with Delivery Leads to ensure staffing, training, tool readiness, and operational consistency.
  • Maintain accurate documentation and clear process controls across teams.

Quality, Analytics & Performance Management

  • Build and maintain reports and dashboards to track accuracy, productivity, error trends, latency, and overall program performance.
  • Conduct root-cause analysis for quality issues and work with QA and Training teams to implement corrective actions.
  • Use data insights to improve annotation quality, reduce rework, and strengthen delivery outcomes.
  • Present clear performance updates to clients and internal stakeholders.

Cross-Functional Collaboration

  • Partner with QA, Training, Delivery, Technical Operations, Business Development, and client-facing teams.
  • Support pilot setup, workflow testing, guideline refinement, schema changes, and ontology updates.
  • Ensure delivery teams understand and apply updated instructions and quality standards.

Continuous Improvement

  • Recommend workflow improvements based on data trends, client feedback, and team input.
  • Support automation opportunities such as pre-labeling, AI-assisted annotation, QC automation, and improved audit methods.
  • Promote consistent ways of working, strong calibration practices, and continuous improvement across programs.

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