As a Manager II, ML Data Operations, you will lead 6-8 Team Managers and oversee large-scale ML data annotation and operational delivery teams across remote and work-from-office environments. This role is responsible for driving operational excellence, people leadership, stakeholder management, quality governance, workforce planning, process optimization, and business continuity across multiple AI/ML programs. You will act as the primary leader responsible for scaling operations, improving productivity, ensuring SLA adherence, and developing high-performing management teams supporting Amazon Robotics and Fulfilment Technologies initiatives.
Key job responsibilities
Lead and manage 6-8 Team Managers overseeing 100–150+ associates across AI/ML data operations programs.
Drive operational excellence by ensuring achievement of key metrics including SLA adherence, productivity, quality, accuracy, utilization, and compliance.
Provide strategic direction and operational governance for multiple annotation and ML data generation programs across image, video, text, dialogue, and robotics-related workflows.
Own workforce planning, capacity management, shift planning, escalation handling, and business continuity management for large-scale operations.
Partner closely with Quality, Training, Capacity Planning, Program Management, and cross-functional stakeholders to improve operational efficiency and customer outcomes.
Drive process improvement initiatives through Lean methodologies, automation opportunities, data-driven decision-making, and operational standardization.
Identify operational risks proactively and implement mitigation strategies to ensure uninterrupted business operations.
Ensure strict adherence to data security, confidentiality, compliance policies, and customer privacy standards.
Drive end-to-end people strategy (hiring, calibration, retention, engagement) and change management initiatives while partnering with Senior leaders on operational roadmaps, analysing performance data to resolve bottlenecks, and owning executive reporting on delivery and organizational health.
Leadership Expectations
Demonstrate strong people leadership and organizational management capabilities.
Build a culture focused on operational excellence, innovation, ownership, and continuous improvement.
Drive bar-raising talent management practices and foster leadership pipeline development.
Operate effectively in highly ambiguous and fast-paced environments while balancing strategic and tactical priorities.
Influence cross-functional teams and stakeholders across geographies and organizational levels.
Lead with customer obsession and data-driven decision-making principles.
A day in the life
You will begin your day by reviewing operational dashboards, SLA risks, workforce coverage, and business escalations across multiple programs. You will conduct leadership syncs with front-line team Managers, review performance metrics, and prioritize operational actions. Throughout the day, you will collaborate with cross-functional stakeholders including Quality, Training, Capacity Planning, Program Management teams to drive operational improvements and resolve blockers. You will participate in business reviews, mentor front-line managers, evaluate process enhancement opportunities, and support strategic planning initiatives that improve operational scalability and quality outcomes.
The role operates in a 24x7 operational environment with rotational shifts and may support both work-from-office and remote operations based on business requirements. Travel may be required occasionally for business reviews, operational support, or stakeholder engagements.
About the team
Amazon Robotics develops state-of-the-art robotics for Amazon Fulfilment Centres and accelerates the future of human-technology interaction at scale. By combining computer vision, machine learning, robotics automation, and advanced operational systems, Amazon Robotics enables high-impact innovations across fulfilment and logistics networks worldwide. Global Operations – Artificial Intelligence (GO-AI) supports this mission by delivering high-quality human-in-the-loop data operations that improve the lifecycle of AI and ML products through near real-time and offline data annotation, validation, and quality operations.
Basic qualifications
- Bachelor's degree in any field of study
- Experience managing large teams of 150 people or more
- Experience building strategic relationships with stakeholders, including communicating and collaborating across teams and functions
- Experience gathering and analyzing large amounts of data and delivering continuous business improvements
- Experience in written and verbal communication skills to communicate with technical and non-technical audiences, including senior leadership
- 8+ years of overall experience with at least 4–5 years of people management experience in Operations, AI/ML support Operations, Annotation services, or large-scale operational environments.
- Proven experience driving operational KPIs including productivity, quality, SLA adherence, compliance, and operational / annotation experience metrics.
- Experience in workforce management, shift planning, escalation management, and business continuity planning.
- Ability to lead remote/hybrid multicultural teams in fast-paced, ambiguous settings with flexibility for rotational shifts and business-critical demands.
Preferred qualifications
- Bachelor's degree in computer science, engineering, mathematics or equivalent, or Master's degree in engineering, statistics, computer science, operations research, business analytics, information systems or equivalent
- Experience in AI/ML operations, data annotation, robotics operations, or human-in-the-loop workflows.
- Exposure to Lean, Six Sigma, operational excellence frameworks, or process automation initiatives.
- Working knowledge of SQL, reporting tools, automation workflows, or operational analytics.
- Experience managing globally distributed operations teams and multiple business programs.
- Strong understanding of machine learning lifecycle support operations and annotation quality frameworks.
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