About the role

ABOUT LILT AI is changing how the world communicates — and LILT is leading that transformation. We're on a mission to make the world's information accessible to everyone, regardless of the language they speak. We use cutting-edge AI, machine translation, and human-in-the-loop expertise to translate content faster, more accurately, and more cost-effectively without compromising on brand, voice, or quality. At LILT, we empower our teammates with leading tools, global collaboration, and growth opportunities to do their best work. Our company virtues—Work together, win together; Find a way or make one; Dance in the customer's shoes; Quicker than they expect; Quality is Job 1—guide everything we do. We are trusted by Intel Corporation https://www.linkedin.com/company/intel-corporation/, Canva https://www.linkedin.com/company/canva/, the United States Department of Defense https://www.linkedin.com/company/deptofdefense/, the United States Air Force https://www.linkedin.com/company/united-states-air-force/, ASICS https://www.linkedin.com/company/asics/, and hundreds of global Enterprises. Backed by Sequoia, Intel Capital, and Redpoint, we’re building a category-defining company in a $50B+ global translation market being redefined by AI. TECHNICAL PROJECT MANAGER — APPLIED AI We are looking for a Technical Project Manager to join our Applied AI team and lead large-scale multilingual data collection, human-in-the-loop workflows, and Large Language Model (LLM) evaluation initiatives. You will work with AI specialists, engineering teams, specialized Subject Matter Experts (SMEs), and data operations teams to translate technical AI requirements into high-throughput data workflows, hybrid evaluation pipelines, and measurable delivery plans. This role combines technical project management with hands-on data analysis, unit economics optimization, and a strong operational understanding of modern LLM training and evaluation processes. KEY RESPONSIBILITIES TECHNICAL PROJECT DELIVERY - Manage Applied AI data collection and evaluation projects from technical scoping through validation, delivery, and retrospective review. through implementation, validation, delivery, and retrospective review. - Translate technical AI requirements into structured data specifications, domain-specific annotation requirements, evaluation plans, and acceptance criteria. - Manage timelines and throughput velocity, dependencies, delivery risks, and technical blockers across Applied AI, engineering, and operations stakeholders. - Coordinate data collection and annotation for supervised fine-tuning (SFT), preference data used in reinforcement learning from human feedback (RLHF), and model evaluation. DATA WORKFLOWS & EVALUATION - Partner with engineering teams to define and validate data workflows, including ingestion, annotation tooling, systems integrations, quality checks, data generation pipelines, and delivery. - Operationalize hybrid evaluation methodologies, combining human review with automated evaluation tooling, and LLM-as-a-judge frameworks. - Coordinate requirements for data formats, schemas, metadata, annotation tools, and system integrations. - Coordinate requirements for data formats, schemas, metadata, annotation tooling, and automated quality checks. - Work with Applied AI specialists to operationalize evaluation methods, including human review, response ranking, and safety testing. - Investigate data, pipeline, or tooling issues and coordinate prompt resolution with technical owners. QUALITY, COST, & PERFORMANCE - Use SQL and business intelligence tools to analyze throughput velocity, quality, unit economics, and supplier/delivery performance. - Define and monitor quality standards, including inter-annotator (IAA) agreement, gold-set performance, and dataset completeness. - Lead root-cause analysis on quality discrepancies, workflow bottlenecks, benchmark discrepancies, and quality edge cases. - Establish deli