Senior ML Data Quality Engineer - RL Training Data

Clera; Singapore; On-site; SGD 190,000 - 317,000; Full time

Benefits offered by this job

Visa sponsorship

Job summary

Clera in Singapore seeks a senior data quality lead who will own data quality strategy and systems for AI/ML data used in reinforcement learning environments. You will guide a small team, define QC standards, and scale validation across thousands of tasks and datasets.

You will collaborate with researchers, engineers, and data vendors to build production-grade data pipelines, dashboards, and experiments that raise data realism, learnability, and reliability for frontier agents.

Qualifications

• 5+ years of experience building AI/ML data evaluation systems.
• Experience leading technical projects or teams on ambiguous problems.
• Advanced proficiency in Python, Docker, and Linux.

Responsibilities

• Lead the data quality team in building systems that evaluate thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows.
• Define data quality strategy by building QC systems, enforcing standards, and designing experiments to grade agent outputs.
• Develop new methods for validating synthetic data at scale, including failure-mode analysis, task mutation checks, and trajectory auditing.
• Partner with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows.
• Turn qualitative research insights into production systems: internal tools, dashboards, validation pipelines, and feedback loops.
• Build internal research intuition around what makes agent training data realistic, learnable, diverse, reliable, and genuinely useful.
• Mentor other research engineers to maintain a high bar for technical rigor, clarity, and execution speed.

Skills

Python

Docker

Linux

ML research

Data quality

QA/QC design

Production pipelines

Tools

Docker & CI/CD

Job description

Clera in Singapore seeks a senior data quality lead who will own data quality strategy and systems for AI/ML data used in reinforcement learning environments. You will guide a small team, define QC standards, and scale validation across thousands of tasks and datasets.

You will collaborate with researchers, engineers, and data vendors to build production-grade data pipelines, dashboards, and experiments that raise data realism, learnability, and reliability for frontier agents.