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Lilt

AI-powered multilingual translation platform for enterprises

Director of Quality

Validation, Compliance & Quality SystemsUpdated on Nov 10
Full-time$170k - $210k /yr

About the Job

Requirements

  • Significant experience running quality systems, including leading internal and external audits, compliance programmes, and writing and maintaining processes and procedures.
  • Experience managing quality for subjective work delivered by expert teams, including writing rubrics and guidelines, calibrating reviewers, and designing sampling-based quality assurance.
  • Hands-on experience designing and building LLM-based agents and partnering with Product and Engineering to take them into production.
  • Experience leading structured continuous improvement, including root cause analysis, corrective and preventive actions, and KPI reviews.
  • Experience leading and developing quality teams, ideally including managers.
  • Ability to translate customer quality requirements into acceptance criteria and governance, advise on quality, cost, and speed trade-offs, and work with customers, executives, and technical teams.
  • Ability to work hands-on with quality data using spreadsheets, business intelligence tools, and SQL, and turn it into actionable metrics, preventive controls, and measurable improvements.

Responsibilities

  • Lead structured continuous improvement across the Localization business through root cause analysis, corrective and preventive actions, KPI reviews, and retrospectives.
  • Write and maintain quality guidelines and error typologies, and run regular calibration so human and AI reviewers apply them consistently.
  • Design sampling plans and quality scorecards that show customers and leadership the quality of outputs.
  • Own a proactive quality metrics framework that makes errors, customer-reported quality issues, rework, and trends visible; use it to prioritize improvements and demonstrate outcomes.
  • Strengthen quality controls across review, delivery, and follow-through, including mandatory account-specific checks, intervention on critical findings, and clear escalation ownership.
  • Partner with Product and Engineering to automate checks and escalation, and verify corrective actions prevent recurrence.
  • Own quality measurement across LILT, including scoring methods, quality targets by account and content type, and the underlying data.
  • Drive quality management for every programme by setting standards, monitoring results, and intervening when quality declines.
  • Partner with Expert Supply and Operations to embed processes, guidance, and quality feedback that maintain a high-quality expert pipeline.
  • Use quality findings and recurring failure patterns to improve expert qualification, guidance, and ongoing quality management.
  • Define what Quality Agents should check and their required performance; build agents using well-defined tools and structured instructions, and partner with Product and Research when deeper engineering or model work is needed.
  • Own the roadmap for Quality Agents as they take on more of the quality workflow.
  • Measure agent accuracy against expert human reviewers, decide where agents can be trusted versus where humans remain in the loop, and establish monitoring and escalation paths.
  • Drive adoption of Quality Agents across teams and demonstrate measurable reductions in manual quality effort.
  • Work with Research and Product on document quality metrics, model improvements, AI review, automated quality assurance, and emerging AI workflows.
  • Design the quality data LILT collects and define metrics so quality scores are consistent, trusted, and useful for evaluating and improving models.
  • Turn quality findings into product requirements, evaluation criteria, and continuous feedback loops that improve model and workflow output.
  • Represent quality in roadmap discussions and help decide where human review, AI review, or both are needed.
  • Own LILT's approach to internal and external audits, including ISO certification and customer audits.
  • Plan and run periodic internal audits, manage evidence collection, and track corrective and preventive actions to closure.
  • Standardize and maintain standard operating procedures, workflows, and quality documentation, keeping them current as AI workflows change.
  • Ensure quality processes meet customer, contractual, and regulatory requirements.
  • Partner with Customer Success and Delivery to define proactive quality programmes, success metrics, and governance for strategic accounts, including AI-forward enterprise and life science or regulatory programmes.
  • Translate customer quality requirements into pre-delivery acceptance criteria, validation methods, and risk-appropriate controls.
  • Advise customers on the appropriate balance of quality, cost, and speed, and on automated, agent-verified, human-verified, or combined workflows.
  • Represent LILT's quality function as a subject-matter authority in customer business reviews, audits, and escalations, using quality data and metrics to demonstrate fitness for purpose.
  • Lead and develop the Quality team.
  • Establish the team's operating cadence and evolve its ways of working as Quality Agents reduce manual coordination work.
  • Oversee embedded quality management staffing sold as a service to customers, ensuring the role owns the specific quality programmes and initiatives customers pay for.
  • Develop playbooks for AI-driven quality management and keep staffing-as-a-service delivery aligned with LILT's quality standards.

Desired Qualifications

  • Quality certifications such as Six Sigma.
  • Background in localization, translation, or language services.
  • Experience in customer-facing quality roles for enterprise accounts.
  • A track record of working with research scientists and product managers to turn quality insight into product requirements and shipped improvements.

About the Company

Lilt

AI-powered multilingual translation platform for enterprises

Lilt provides an AI-powered translation platform for enterprises to manage multilingual content. It uses a Contextual AI Engine with a human-in-the-loop: AI translations are reviewed by professional linguists, and feedback helps the model improve. It supports 70+ languages and formats and integrates with 100+ enterprise systems, sold as a subscription SaaS (plus direct translation services). Its goal is to speed up high-volume translation while ensuring accuracy and consistency across global content.

Company size
501-1,000
Company stage
Series C
Total funding
$95.6M
Headquarters
San Francisco, CA
Founded
2015

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