Technical Training and Quality Manager, AI Data

Innodata Inc.; Philippines; Hybrid; PHP 1,200,000 - 1,800,000; Full time

Benefits offered by this job

Flexible work arrangements

Remote or onsite options

Job summary

Innodata Inc. in the Philippines seeks a Technical Training and Quality Manager - AI Data to lead training design and QA programs for AI data annotation teams, bridging ML/AI requirements with operations.

You will own guidelines, develop curricula, calibrate QA, and collaborate with ML engineers, linguists, and PMs to ensure high-quality data and scalable training across sites. Ideal candidates combine 7+ years in training/QA, Lean Six Sigma, and a deep understanding of AI data lifecycle, with

Qualifications

• Bachelor's degree in Computer Science, Linguistics, Education, or related field.
• 7+ years in training, QA, or ops in AI data annotation or similar.
• Lean Six Sigma Green Belt certification.
• Strong understanding of AI/ML data lifecycle (labeling, annotation, RLHF, evaluation).
• Experience designing curricula and QA frameworks for high-volume operations.
• Excellent communication and stakeholder management skills.
• Analytical mindset using quality data to drive decisions.

Responsibilities

• Design, develop, and deliver technical training for annotators, reviewers, and QA staff.
• Create onboarding curricula, manuals, and certifications for new hires.
• Update training content with evolving guidelines and tooling changes.
• Conduct train-the-trainer sessions across shifts/sites/regions.
• Track training effectiveness through assessments and pilot tests.
• Define and maintain quality rubrics and scoring guidelines.
• Design and implement QA frameworks (sampling plans, audits, calibration sessions, inter-annotator agreement checks).
• Monitor quality metrics and drive root-cause analysis for defects/errors.
• Partner with data scientists/ML engineers to translate model feedback into improvements.
• Lead calibration sessions to align annotators/reviewers on edge cases.
• Own guideline documents, SOPs, and knowledge bases for each project/task type.
• Identify gaps in guidelines and drive clarification with stakeholders.
• Build feedback loops between quality findings and training content updates.
• Standardize quality and training processes across multiple projects/programs.
• Work with PMs, ML engineers, linguists, and operations teams.
• Report on training completion, quality trends, and risk areas to leadership.
• Advise on staffing/skill requirements based on task complexity.

Skills

Training design

Quality assurance

Stakeholder management

Communication

Facilitation

Analytical thinking

Education

Bachelor's degree in Computer Science / Linguistics / Education

Lean Six Sigma Green Belt

Tools

AI/ML labeling tools

QA tooling

Job description

About the Role

As Technical Training and Quality Manager - AI Data, you will lead the training design and quality assurance programs for teams working on AI data annotation, labeling, evaluation, or model training data pipelines. You will bridge the gap between technical requirements (ML/AI teams) and operational execution (annotator/reviewer workforce)

Key Responsibilities

Training & Enablement

• Design, develop, and deliver technical training programs for annotators, reviewers, and QA staff on AI/ML data labeling tools, guidelines, and workflows
• Create onboarding curricula, training manuals, job aids, and certification programs for new hires
• Continuously update training content based on evolving project guidelines, tooling changes, and model requirements
• Conduct train-the-trainer sessions to scale training delivery across shifts/sites/regions
• Track training effectiveness through assessments, pilot tests, and performance data

Quality Management

• Define and maintain quality standards, rubrics, and scoring guidelines for data annotation/labeling tasks
• Design and implement QA frameworks (sampling plans, audits, calibration sessions, inter-annotator agreement checks)
• Monitor quality metrics (accuracy, consistency, throughput) and drive root-cause analysis for defects/errors
• Partner with data scientists/ML engineers to translate model feedback into actionable quality improvements
• Lead calibration sessions to align annotators/reviewers on edge cases and ambiguous guidelines

Process & Documentation

• Own and maintain guideline documents, SOPs, and knowledge bases for each project/task type
• Identify gaps in guidelines and drive clarification with project/product stakeholders
• Build feedback loops between quality findings and training content updates
• Standardize quality and training processes across multiple projects/programs

Cross-functional Collaboration

• Work closely with Project/Program Managers, ML Engineers, Linguists, and Operations teams
• Represent training/quality needs in project kickoffs and guideline design discussions
• Report on training completion, quality trends, and risk areas to leadership
• Support workforce planning by advising on staffing/skill requirements based on task complexity

Required Qualifications

• Bachelor's degree in a relevant field (Computer Science, Linguistics, Education, or related); equivalent experience considered
• 7+ years of experience in training, quality assurance, or operations management, ideally within AI/ML data annotation, BPO, or tech-enabled services
• Certification in Lean Six Sigma Green Belt
• Strong understanding of AI/ML data lifecycle (labeling, annotation, RLHF, evaluation, etc.)
• Experience designing training curricula and QA frameworks for high-volume operations
• Excellent communication, facilitation, and stakeholder management skills
• Analytical mindset with experience using quality/performance data to drive decisions

DIVERSITY, EQUITY, INCLUSION & BELONGING

At Innodata, Diversity, Equity, Inclusion, and Belonging are at the heart of who we are. With over 35 years of expertise, we foster an inclusive workplace that celebrates the rich diversity of backgrounds, experiences, and perspectives our employees bring — diversity that fuels our innovation in AI and data engineering. We encourage individuals from all walks of life to apply, particularly those who may feel they don't meet every qualification or who hesitate due to their identity. Through inclusive learning initiatives, Employee Resource Groups, and flexible work arrangements, we create a sense of belonging for every team member, whether remote or onsite.

How to Apply

Apply directly through this posting, and our team will be in touch.