Research Engineer, Benchmarks
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
- Create and upkeep internal tests that measure AI agents on tasks specific to a domain
- Work with subject experts to turn everyday workflows into test scenarios and standards for judging them
- Build dependable infrastructure that runs models and agents through benchmark tasks at scale
- Use metrics and analysis to examine benchmark challenge, consistency and failure patterns, then compare results with real-world evaluations
- Write technical guides and reports that explain benchmark methods and findings
What you bring
- Two to four years in software engineering, machine learning engineering or research, with two years focused on AI benchmarks or evaluation systems
- A technical degree and strong command of Python, Docker and Linux
- You have designed evaluation settings for AI agents or large language models and built systems to run them reliably
- You can develop measures and statistical analyses, validate results, and shape benchmark tasks with subject experts
Who this fits
This mid-level, hands-on role suits someone who can move between engineering, evaluation design and research analysis. You’ll work on-site in Singapore, and visa sponsorship is available.
From the employer
About the Role
Join a small, technical team in the AI evaluation sector to design and build rigorous benchmarks for frontier AI agents. Your work will help ensure evaluations reflect realistic workflows and provide credible insights into real-world agent performance.
What You'll Do
Design, implement, and maintain internal benchmarks for evaluating AI agents on domain-specific tasks.
Work with subject-matter experts to translate real workflows into evaluation tasks and criteria.
Build reliable infrastructure to run models and agents against benchmark tasks at scale.
Develop metrics and analyses to assess benchmark difficulty, reliability, and failure modes.
Validate how benchmark results relate to real-world evaluations and user needs.
Write clear technical documentation and benchmark reports.
What We're Looking For
Two to four years of experience in software engineering, machine learning engineering, or research, including at least two years building AI benchmarks, evaluation infrastructure, or agent environments.
A degree in a technical field and strong proficiency in Python, Docker, and Linux.
Experience designing and operating benchmark or evaluation environments for AI agents or large language models.
Background building infrastructure to run AI models or agents reliably, and developing metrics, statistical analyses, or validation studies.
Experience collaborating with subject-matter experts to model workflows across technical or business domains and create benchmark tasks.
Strong technical writing, attention to detail, and ability to reason from first principles in an early-stage, unstructured environment.
Published research or technical writing on AI benchmarking, model evaluation, or failure modes is valued. Reinforcement learning experience is also useful.
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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