Research Engineer, Synthetic Data (Mid-Level)

Clera; Singapore; On-site; SGD 128,000 - 218,000; Full time

Benefits offered by this job

Visa sponsorship available

Job summary

Clera on-site in Singapore is seeking an engineer to join an AI evaluation and reinforcement learning team. You will transform real-world workflows into scalable, synthetic training tasks and devise metrics to measure improvements in model capabilities.

You will build end-to-end generation pipelines, work with domain SMEs, and develop tools to mutate, validate, and enhance synthetic tasks while analyzing agent performance to identify what helps learning and where gaps remain.

Qualifications

• Two or more years of software engineering, ML engineering, or AI research experience with data pipelines or ML infrastructure.
• Hands-on experience applying synthetic data methods to end-to-end generation pipelines.
• Proficiency in Python and Linux environments with Docker.
• Understanding of synthetic data quality, evaluation metrics, and data limitations.
• Experience building evaluation benchmarks, frameworks, or testing environments for AI agents or language models.

Responsibilities

• Build pipelines that transform workflows into synthetic training tasks.
• Collaborate with subject-matter experts to create tasks across domains.
• Develop methods and tools to generate, mutate, validate, and improve synthetic tasks.
• Analyze agent performance to learn what tasks teach and where they fail.
• Create metrics for task diversity, realism, learnability, and quality.

Skills

Python

Linux

Docker

Data pipelines

ML infrastructure

Evaluation frameworks

Job description

About the Role

Join an AI evaluation and reinforcement learning team building synthetic data pipelines for AI agents. You will turn real-world professional workflows into scalable training tasks and develop ways to measure whether those tasks improve model capabilities.

What You'll Do

• Build pipelines that transform domain-specific workflows into realistic, structured, and challenging synthetic training tasks.
• Collaborate with subject-matter experts to create tasks across professional and technical domains.
• Develop methods and tools to generate, mutate, validate, and improve synthetic tasks.
• Analyze agent performance to understand what tasks teach and where they fail.
• Create metrics for task diversity, realism, learnability, and overall quality.

What We're Looking For

• Two or more years in software engineering, machine learning engineering, or AI research, with relevant experience building data pipelines or ML infrastructure.
• Hands-on experience applying synthetic data research methods to build end-to-end generation pipelines.
• Proficiency in Python and experience working in Linux environments with Docker.
• Understanding of synthetic data quality, evaluation metrics, and the limitations of generated data.
• Experience building evaluation frameworks, benchmarks, or testing environments for AI agents or language models.
• Ability to independently deliver technical projects, build automated data systems, and spot subtle quality issues and edge cases.
• Familiarity with reinforcement learning, agent workflows, or model post-training is useful.

Compensation & Benefits

Salary range: USD 100,000 to 170,000 annually. Visa sponsorship is available.

Location

On-site in Singapore, Singapore.