Research Engineer, QC Automation
At a glance
Mid-level AI Research role at Clera. Singapore · full-time · $100,000–$170,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build automated checks for training data created through the company’s infrastructure
- Set data-quality standards through human review and analysis, with limited reliance on large language models
- Create experiments and metrics to judge agent results and evaluation reliability
- Investigate data problems with vendors, trace agent failures, and improve how data is generated
- Turn findings into audits, sampling plans, and validation pipelines using rules or models
What you bring
- Two to four years in research engineering or a related role focused on automating quality control, plus a technical degree
- Proficiency in Python, Docker, and Linux
- A track record of building scalable data checks and complete automated QA or QC systems independently
- Experience with reinforcement-learning data benchmarks and evaluations, including task design, rubrics, and training trajectories
- Strong statistics and experience measuring data quality and designing experiments, metrics, and quality processes
Who this fits
This suits a mid-level research engineer who can work independently in an unstructured environment and communicate across time zones. The role is on-site in Singapore, and visa sponsorship is available.
From the employer
About the Role
Join an engineering team at an early-stage AI infrastructure company focused on reinforcement learning environments and evaluation. You will build quality-control systems for training data, helping ensure that data and evaluations are reliable as demand grows.
What You'll Do
Automate quality control for training data produced using the company's infrastructure.
Define and enforce clear quality standards using human judgment and careful analysis, rather than relying heavily on large language models.
Design experiments and metrics to assess agent outputs and evaluation quality.
Work with data vendors to investigate quality issues, diagnose agent failure modes, and improve data-generation processes.
Turn quality-control findings into auditing systems, sampling strategies, and rule-based or model-assisted validation pipelines.
Improve infrastructure tools and vendor-facing workflows to reduce anomalies, inconsistencies, and edge cases.
What We're Looking For
Two to four years of experience in a research engineering or similar role focused on quality-control automation, plus a degree in a technical field.
Proficiency with Python, Docker, and Linux.
Experience independently building scalable data-validation pipelines and end-to-end automated QA or QC systems.
Background in benchmarks and evaluations for reinforcement learning data, including realistic task design, reliable rubrics, and useful training trajectories.
Experience defining and measuring training-data quality, and designing experiments, metrics, and quality processes.
Strong statistical knowledge and the ability to communicate clearly across time zones and work independently in an unstructured startup environment.
Compensation & Benefits
Base salary: $100,000 to $170,000 USD annually. Visa sponsorship is available.
Location
On-site in Singapore, Singapore.
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