Project Manager, Applied AI

Location: Global: Europe, Asia-Pac, Argentina, Mexico Department: Production (Applied AI) Reports to: Program Director Rate: $20 per hour We are looking for a data-driven Project Managers to lead our large-scale multilingual data collection and Large Language Model (LLM) evaluation initiatives. In this role, you will be the operational backbone of our AI development, orchestrating global teams of annotators and data specialists. If you thrive in a fast-paced environment where you can optimize workflows for productivity, quality, and throughput, we want to hear from you Key Responsibilities • Project Management • End-to-End Delivery: Manage the full lifecycle of AI data projects, from scoping and guidelines creation to data delivery and post-mortem analysis. • Pipeline Management: Oversee large-scale data pipelines for multilingual data collection (audio, text, image) and LLM evaluation (RLHF, SFT, ranking, and safety testing). • Quality Assurance & Performance Monitoring • KPI Tracking: rigorously monitor and report on key performance indicators, including: • Throughput: Volume of data processed per hour/day. • Quality: Accuracy scores, Inter-Annotator Agreement (IAA), and gold-set performance. • Productivity: Cost-per-task and worker efficiency rates. • Quality Control: Run QA loops, root-cause analysis for quality dips, and corrective training for annotator pools. • Dashboards: Maintain dashboards to visualize project health and flag bottlenecks in real-time. • Stakeholder Management • Global Coordination: Manage relationships with data experts and crowd pools, ensuring adherence to SLAs regarding localized nuances and linguistic accuracy. • Cross-Functional Collaboration: Liaise with Applied AI Technical Ops teams. Translate technical requirements into clear, actionable guidelines for non-technical annotators. • Feedback Loops: Facilitate continuous feedback loops where data insights drive updates to annotation guidelines and model fine-tuning strategies. Qualifications Essential Skills & Experience • Experience: 3-5+ years of project management experience, specifically within AI/ML data operations. • LLM Knowledge: Strong understanding of LLM training processes (Pre-training, SFT, RLHF) and evaluation methodologies (Human-in-the-loop, red teaming). • Data Proficiency: Advanced proficiency in Excel/Google Sheets; ability to write SQL queries to extract and analyze performance data. • Methodology: Proven track record using Agile, Scrum, or Kanban methodologies to manage complex workflows. • Communication: Exceptional ability to write clear, unambiguous guidelines for multilingual audiences. Preferred Qualifications (Nice To Haves) • Multilingual: Fluency in a second language is highly desirable. • Technical Tools: Experience with data annotation platforms (e.g. Scale AI, Super Annotate) and project management tools (e.g. Jira). • Education: Background in ML Engineering, Computer Science, Data Science and Project Management training. AI is changing how the world communicates — and LILT is leading that transformation. LILT's mission is to make the world's information available to everyone, no matter the language they speak. Join our global community who thrive on innovation and excellence. Our collective knowledge, uniqueness, and skills deliver multilingual AI and human-verified services to Enterprises, Governments, and AI Developers around the world. Earn money. Have fun. Advance human knowledge. Work on diverse projects from anywhere, any time you want. Get paid quickly and fairly, and build your professional network in a supportive community—all through a streamlined application process tailored to your expertise. Information collected and processed as part of your application process, including any job applications you choose to submit, is subject to LILT's Privacy Policy at https://lilt.com/legal/privacy. At LILT, we are committed to a fair, inclusive, and transparent hiring process. As part of our recruitment efforts, we may use artificial intelligence (AI) and automated tools to assist in the evaluation of applications, including résumé screening, assessment scoring, and interview analysis. These tools are designed to support human decision-making and help us identify qualified candidates efficiently and objectively. All final hiring decisions are made by people. If you have any concerns, require accommodations, or would like to opt-out of the use of AI in our hiring process, please let us know at [email protected]. LILT is an equal opportunity employer. We extend equal opportunity to all individuals without regard to an individual’s race, religion, color, national origin, ancestry, sex, sexual orientation, gender identity, age, physical or mental disability, medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other classification protected by applicable local, state or federal laws. We are committed to the principles of fair employment and the elimination of all discriminatory practices. Show more Show less

Experience: 3-5+ years of project management experience, specifically within AI/ML data operations. LLM Knowledge: Strong understanding of LLM training processes (Pre-training, SFT, RLHF) and evaluation methodologies (Human-in-the-loop, red teaming). Data Proficiency: Advanced proficiency in Excel/Google Sheets; ability to write SQL queries to extract and analyze performance data. Methodology: Proven track record using Agile, Scrum, or Kanban methodologies to manage complex workflows. Communication: Exceptional ability to write clear, unambiguous guidelines for multilingual audiences.

Project Management; End-to-End Delivery: Manage the full lifecycle of AI data projects, from scoping and guidelines creation to data delivery and post-mortem analysis; Pipeline Management: Oversee large-scale data pipelines for multilingual data collection (audio, text, image) and LLM evaluation (RLHF, SFT, ranking, and safety testing); Quality Assurance & Performance Monitoring; KPI Tracking: rigorously monitor and report on key performance indicators, including Throughput, Quality (IAA, gold-set performance), Productivity (cost-per-task, worker efficiency); Quality Control: Run QA loops, root-cause analysis for quality dips, and corrective training for annotator pools; Dashboards: Maintain dashboards to visualize project health and flag bottlenecks in real-time; Stakeholder Management; Global Coordination: Manage relationships with data experts and crowd pools, ensuring adherence to SLAs regarding localized nuances and linguistic accuracy; Cross-Functional Collaboration: Liaise with Applied AI Technical Ops teams; Translate technical requirements into clear, actionable guidelines for non-technical annotators; Feedback Loops: Facilitate continuous feedback loops where data insights drive updates to annotation guidelines and model fine-tuning strategies.