Project Manager, Applied AI

LILT (Production); Polska; Hybrid; PLN 88,000 - 126,000; Full time

Job summary

LILT (Production) is seeking a data-driven Project Manager to lead large-scale multilingual data collection and LLM evaluation initiatives. You will be the operational backbone of our AI development, coordinating global teams of annotators and data specialists to deliver on ambitious timelines.

In a fast-paced environment you will optimize workflows for productivity, quality and throughput, track KPIs, and translate technical requirements into actionable guidelines for non-technical staff while

Qualifications

• Experience leading AI data projects across multilingual datasets.
• Ability to translate guidelines for annotators and data specialists.
• Proven track record in coordinating global teams and meeting SLAs.

Responsibilities

• Lead end-to-end AI data projects from scoping to data delivery and post-mortem analysis.
• Oversee large-scale data pipelines for multilingual data collection and LLM evaluation.
• Monitor KPIs, report productivity and quality, and maintain real-time dashboards.

Skills

Project management

Global coordination

Stakeholder management

KPI tracking

Job description

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