Technical Lead, AI Data Operations

We are seeking experienced Technical Leads, AI Data Operations to oversee a growing team of Data Annotation Specialists supporting large-scale AI training and data quality initiatives.

The initial emphasis will be on computer vision and multimodal data programs, including image and video annotation, while the team may expand into additional modalities such as text, audio, sensor, geospatial, Generative AI evaluation, and other AI training data workflows.

Technical Leads will be responsible for maintaining dataset quality, driving operational excellence, mentoring annotators, resolving complex annotation scenarios, and partnering with researchers and technical stakeholders to ensure successful project execution.

This role requires someone who can balance people leadership, quality management, data operations, and technical problem-solving while helping scale annotation operations from an initial team of approximately 20 specialists to potentially 50-100+ contributors.

Day-to-Day Responsibilities

Team Leadership

Lead and mentor Data Annotation Specialists.

Conduct ongoing performance coaching and quality reviews.

Support interviewing and hiring activities.

Drive onboarding and training programs for new hires.

Establish clear expectations around quality, productivity, and guideline adherence.

Quality & Data Operations

Audit annotation outputs across AI training datasets.

Oversee quality across image, video, and other supported data modalities.

Monitor quality, accuracy, throughput, productivity, and compliance metrics.

Identify annotation and data quality issues and determine root causes.

Deliver corrective feedback and calibration guidance.

Maintain dataset integrity and consistency across projects.

Resolve complex or ambiguous annotation scenarios.

Help establish quality thresholds and escalation processes.

Cross-Functional Partnership

Partner with AI researchers, engineers, project managers, and program stakeholders.

Review ambiguous annotation scenarios and establish operational guidance.

Translate technical requirements into clear instructions for annotation teams.

Escalate project risks, data quality concerns, and guideline gaps.

Communicate trends and insights from annotation operations back to technical stakeholders.

Process Improvement

Analyze trends across quality, throughput, and operational performance.

Recommend enhancements to tooling, workflows, annotation guidelines, and QA processes.

Contribute to internal platform and process improvements.

Develop documentation, best practices, training materials, and knowledge resources.

Produce reporting and operational updates for leadership.

Support the expansion of annotation operations into new data modalities and AI use cases.

Screening Assessment

Candidates should be able to demonstrate the ability to:

Identify labeling and data quality issues and determine likely root causes.

Deliver clear, actionable feedback to annotators.

Prioritize competing workloads and deadlines.

Navigate ambiguous guidelines and establish appropriate escalation paths.

Communicate project risks and quality trends to leadership and technical stakeholders.

Translate technical requirements into scalable annotation workflows.

Drive continuous improvement initiatives across quality and operational performance.

Success Profile

The ideal Technical Lead is highly analytical, operationally minded, and capable of bridging the gap between AI research teams and annotation operations.

They excel at developing people, identifying quality risks before they become systemic problems, and building scalable annotation processes across evolving AI data programs. While computer vision and multimodal data will be the initial focus, they are capable of applying the same quality and operational frameworks across new modalities and AI training use cases as the program expands.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global’s Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Must-Have Qualifications

Bachelor’s Degree required.

U.S. Citizen required.

2-5+ years of experience leading teams or programs within:

Data Annotation

AI Data Operations

Computer Vision Operations

AI Training Data

Data Quality Programs

Content Operations

Model Evaluation

Experience performing quality audits, dataset reviews, or annotation quality analysis.

Experience onboarding, coaching, and mentoring team members.

Demonstrated success improving quality, productivity, and operational performance metrics.

Strong understanding of annotation workflows, guideline development, quality management, and dataset governance.

Experience conducting root cause analysis and driving corrective actions.

Ability to manage competing priorities across multiple workstreams.

Strong written and verbal communication skills.

Experience partnering with researchers, engineers, project managers, or other technical stakeholders.

Ability to translate ambiguous or technical requirements into clear, actionable instructions for annotation teams. Preferred Qualifications

Experience with Computer Vision, Visual AI, or multimodal datasets.

Experience with:

Bounding box annotation

Segmentation workflows

Classification programs

Object tracking

Visual quality validation

Experience working with additional data modalities such as text, audio, sensor, geospatial, or multimodal data.

SQL and/or Python experience.

Experience designing annotation standards, taxonomies, guidelines, and quality frameworks.

Experience with workforce planning and resource allocation.

Experience scaling annotation or AI data operations teams.

Experience building QA methodologies, calibration processes, and quality reporting.

Familiarity with AI model evaluation, Generative AI, RLHF, preference data, or human-feedback programs.