Project Manager, Applied AI

LILT

9d

About The Position

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.

Requirements

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

Nice To Haves

• Fluency in a second language is highly desirable.
• Experience with data annotation platforms (e.g. Scale AI, Super Annotate) and project management tools (e.g. Jira).
• Background in ML Engineering, Computer Science, Data Science and Project Management training.

Responsibilities

• Manage the full lifecycle of AI data projects, from scoping and guidelines creation to data delivery and post-mortem analysis.
• Oversee large-scale data pipelines for multilingual data collection (audio, text, image) and LLM evaluation (RLHF, SFT, ranking, and safety testing).
• Rigorously monitor and report on key performance indicators, including throughput, quality, and productivity.
• Run QA loops, root-cause analysis for quality dips, and corrective training for annotator pools.
• Maintain dashboards to visualize project health and flag bottlenecks in real-time.
• Manage relationships with data experts and crowd pools, ensuring adherence to SLAs regarding localized nuances and linguistic accuracy.
• Liaise with Applied AI Technical Ops teams.
• Translate technical requirements into clear, actionable guidelines for non-technical annotators.
• Facilitate continuous feedback loops where data insights drive updates to annotation guidelines and model fine-tuning strategies.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed