Project Manager, Applied AI

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.

Key Responsibilities

1. 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).

2. 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.

3. 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.