Jobs at Welo Global

Audit and ensure quality of data labeling work for ML using human-in-the-loop processes. Provide feedback, train and support annotators, monitor workflows, identify recurring issues, and help improve guidelines and processes.

Lead and own robotics safety and compliance strategy, define SOPs and KPIs, conduct audits and RCAs, manage incidents and CAPA, deliver safety training, enforce operational controls, and engage clients on safety performance.

Perform on-site data annotation and QA for natural language datasets, test AI products, analyze data for consistency and relevance, report anomalies, support ML model updates, and prepare concise reports on data quality and model performance.

Audit and review data labeling outputs, provide actionable feedback, train and support labeling teams, monitor workflows, identify recurring issues and edge cases, participate in calibrations and stakeholder syncs, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and improve data labeling quality in human-in-the-loop ML workflows. Coach and onboard labelers, provide actionable feedback, monitor queues, identify recurring issues, and help refine guidelines and processes.

Evaluate AI model outputs, perform audit-style reviews to find patterns and failure modes, apply and refine labeling/evaluation guidelines, document findings, communicate insights to leads and stakeholders, and participate in team calibrations to improve model evaluation and data quality.

Audit and ensure quality of labeled data by reviewing DLA outputs, providing feedback and coaching, supporting onboarding and training, monitoring workflows, identifying recurring issues and edge cases, and contributing to guideline and process improvements in human-in-the-loop AI workflows.

Audit and validate data labeling outputs, provide structured feedback and hands-on training to annotators, monitor workflows and escalate issues, help apply and improve guidelines, and identify patterns in human and model outputs to maintain high-quality AI training data.

Evaluate AI model outputs, perform audit-based reviews, apply and improve guidelines, identify trends and failure modes, document findings, and collaborate with leads and cross-functional teams to refine quality standards.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, apply and refine guidelines, monitor workflows and escalate issues, document findings, and support human-in-the-loop AI evaluation processes.

Evaluate AI model outputs, perform audit-based reviews, identify failure modes and trends, apply and improve guidelines, document findings, and collaborate with leads and cross-functional teams to refine quality standards.

Perform Chinese annotation, labeling, and quality review of multimodal datasets (text, image, video, audio) for generative AI. Evaluate AI prompts/responses, conduct QA, identify edge cases, refine guidelines, and collaborate with teams to improve annotation workflows and tooling.

Evaluate AI outputs and audit model behavior, apply and refine guidelines, identify failure modes and trends, document findings, and collaborate with leads and cross-functional teams to improve evaluation frameworks and data quality.

Audit and ensure quality of data labeling outputs, coach and onboard Data Labeling Associates, provide actionable feedback, monitor workflows, identify patterns and edge cases, and help improve guidelines and processes in human-in-the-loop AI workflows.

Evaluate AI outputs, perform audit-based reviews, identify patterns and failure modes, apply and improve annotation/evaluation guidelines, document findings, and communicate insights to cross-functional teams to improve model performance and evaluation frameworks.

Lead and manage a team of Chinese (zh-CN) multimodal annotators and reviewers to meet throughput and quality targets. Own guidelines, calibration, audits, reporting, onboarding, and cross-stakeholder coordination for multimedia labeling workflows.

Evaluate AI outputs, perform audit-based reviews, identify failure modes and trends, apply and refine guidelines, communicate findings, participate in calibrations and stakeholder syncs, and help improve evaluation frameworks for human-in-the-loop AI.

Review and proofread French Canadian localized content for accuracy, consistency, and cultural appropriateness. Execute test cases, identify and report localization bugs in bug-tracking systems, verify fixes, and occasionally translate English to French (Canada). Follow project style guides, troubleshoot basic issues, and ensure high-quality localized user experiences.

Perform Czech localization QA and proofreading onsite: execute test cases, identify and report localization bugs, verify fixes, and ensure linguistic quality across Mac OS X and iOS web-based applications while collaborating with diverse teams.

Audit and monitor data labeling outputs, provide actionable feedback, support onboarding and training of annotators, enforce and improve guidelines, escalate workflow issues, and document findings to maintain high-quality human-in-the-loop AI data processes.