About Argos Multilingual
Argos Multilingual is a global language, data, and AI services company helping leading organizations build, evaluate, and improve AI systems.
Our Data Services team partners with AI labs, technology companies, and enterprise AI teams on complex human data programs across multilingual evaluation, speech and audio, model response evaluation, expert review, annotation, and emerging agentic workflows.
As AI systems become more capable and more complex, high-quality human evaluation is becoming a critical part of how models are trained, tested, and improved. We are building a Data Services organization focused on quality, scalability, operational excellence, and customer trust.
Summary of the role
The Senior Manager, Data Quality to helps build the quality engine behind Argos’ AI Data Services business.
In this role, you will define how we evaluate, calibrate, measure, and scale high-quality human data programs for leading AI companies. You will design quality frameworks for data collection, annotation, evaluation, review, calibration, adjudication, and customer reporting.
You will collaborate closely with Program Management, Supply Chain, Solutions, Sales, and customer-facing teams to turn customer requirements into clear, scalable quality workflows. You will help ensure that every program has the right evaluation methodology, task instructions, reviewer calibration, sampling approach, escalation process, and performance reporting in place.
This is a high-impact role for someone who enjoys building systems, improving quality, working cross-functionally, and operating in a fast-moving AI services environment.
Responsibilities
Build quality systems for AI data programs
- Design and manage quality frameworks for AI data and evaluation programs.
- Translate customer requirements into clear quality standards, rubrics, acceptance criteria, review processes, and KPIs.
- Build quality workflows that are practical, scalable, and trusted by customers as programs move from pilot to production.
- Identify quality risks early and work with delivery teams to resolve issues before they impact timelines, customer confidence, or program outcomes.
- Create repeatable quality processes across calibration, QA sampling, adjudication, reviewer performance tracking, and customer reporting.
Lead evaluation, calibration, and QA processes
- Support quality operations across multilingual evaluation, speech/audio QA, transcription, data annotation, human preference evaluation, expert review, model response evaluation, coding evaluation, tool-use evaluation, and agent workflow evaluation.
- Create and improve rubrics, task instructions, reviewer guides, calibration exercises, golden datasets, and quality reporting templates.
- Lead calibration sessions with reviewers, annotators, quality specialists, delivery teams, and customer stakeholders.
- Define quality thresholds, error taxonomies, escalation rules, and corrective action plans.
- Monitor reviewer agreement, disagreement trends, error rates, contributor performance, and root causes of quality variance.
- Turn QA findings into practical improvements to instructions, training, tooling, staffing, and delivery workflows.
Partner with customers and internal teams
- Act as a quality lead for strategic customer programs when needed.
- Support customer-facing quality readouts, pilot retrospectives, business reviews, escalations, and scale-up discussions.
- Provide clear, data-backed reporting that explains quality performance, risks, corrective actions, and next steps.
- Partner with Program Management, Supply Chain, Solutions, Sales, and Operations to ensure programs are set up for quality success from the start.
- Work with Supply Chain to define reviewer profiles, evaluator requirements, language requirements, domain expertise, onboarding needs, and performance expectations.
- Help determine when programs require expert reviewers, QA leads, language leads, technical reviewers, or specialized evaluation talent.
Build and develop the quality function
- Build reusable quality assets such as calibration packs, QA reports, rubric libraries, error taxonomies, scorecards, and sample evaluation frameworks.
- Identify repeatable patterns across programs and turn them into standardized approaches that help the business scale.
- Improve visibility into quality performance across programs, reviewers, contributors, and workflows.
- Manage, coach, and support Quality Managers, Quality Leads, Quality Specialists, reviewers, or QA contributors assigned to Data Services programs.
- Coach team members on quality judgment, customer communication, escalation handling, reporting, and root-cause analysis.
- Identify hiring, training, and coverage needs as the Data Services business grows.
- Create a culture of quality ownership, accountability, and continuous improvement.
People management
- Anticipate and communicate the needs identified for the team under your responsibility.
- Train, and support team members, advocate for upskilling and promote career growth.
- Be responsible for offering help to team members during increased workload periods, helping to avoid risks to Client deliveries due to their time demands (this includes finding cover for sickness and absence).
Qualifications
Education, skills, and experience
- 5+ years of experience in quality operations, data operations, AI data services, localization quality, annotation quality, evaluation operations, trust and safety quality, or a related field.
- Experience managing quality programs for complex customer accounts or high-volume operational delivery.
- Strong understanding of QA methodologies, calibration, sampling, adjudication, error analysis, and performance reporting.
- Experience working cross-functionally with delivery, operations, supply chain, sales, and customer-facing teams.
- Strong analytical skills and the ability to turn quality data into clear operational improvements.
- Excellent written and verbal communication skills, including the ability to communicate quality issues clearly to customers and senior stakeholders.
- Comfort operating in fast-moving, ambiguous environments where processes are still being built.
- Strong people leadership skills with experience coaching quality specialists, reviewers, annotators, or operational teams.
Nice to have
- Experience with AI data, RLHF, model evaluation, LLM evaluation, speech/audio evaluation, transcription, coding evaluation, multilingual evaluation, or expert review programs.
- Experience designing rubrics, annotation guidelines, evaluation instructions, reviewer training, calibration workflows, or quality scorecards.
- Experience supporting AI labs, enterprise AI teams, research teams, or technical customers.
- Familiarity with human-in-the-loop data workflows, annotation platforms, QA tooling, dashboards, and data labeling operations.
- Experience working with expert contributors, linguists, annotators, domain specialists, technical reviewers, or distributed talent networks.
- Knowledge of multilingual evaluation, speech/audio QA, cultural appropriateness, or language-specific quality risks.
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