# Research Engineer, Synthetic Data at Clera

Research Engineer, Synthetic Data at Clera in Singapore. On-site, full-time, paying $100k–$170k. Posted November 4, 2026.

- Company: Clera
- Location: Singapore
- Workplace: On-site
- Job type: Full-time
- Pay: $100k–$170k
- Posted: 2026-11-04
- Apply: https://jobs.ashbyhq.com/clera/4955b2ea-95c2-4a78-bacf-02abd3b994e6/application
- Source: https://jobmagic.ai/jobs/research-engineer-synthetic-data-clera-3wpj0g

## Requirements

- Two or more years of relevant experience in software engineering, machine learning engineering, or AI research, with a technical background.
- Hands-on experience applying synthetic data methods and delivering end-to-end data generation pipelines for AI or machine learning applications.
- Proficiency in Python and experience working in Linux environments with containerization tools such as Docker.
- Experience with synthetic data quality criteria, evaluation metrics, and the limitations of synthetic data.
- Background building or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.
- Experience automating the generation, validation, mutation, or processing of structured datasets at scale.
- Ability to independently deliver technical projects in loosely defined problem spaces, with care for edge cases and data quality.
- Familiarity with reinforcement learning, agent workflows, or LLM post-training is useful.

## Description

### About the Role

Join a small engineering team building synthetic data systems for AI agents. You will turn real-world professional workflows into scalable training tasks, helping improve how models learn and perform across practical domains.

### What You'll Do

- Build pipelines that transform domain-specific workflows into realistic, structured synthetic training tasks.

- Work with subject-matter experts to create tasks for AI agents across professional and technical domains.

- Design generation methods that produce diverse, realistic, and learnable tasks.

- Develop tools to mutate, validate, and improve synthetic tasks.

- Analyze model and agent performance to understand task strengths, limitations, and failure modes.

- Create metrics for task diversity, realism, learnability, and overall quality.

### What We're Looking For

- Two or more years of relevant experience in software engineering, machine learning engineering, or AI research, with a technical background.

- Hands-on experience applying synthetic data methods and delivering end-to-end data generation pipelines for AI or machine learning applications.

- Proficiency in Python and experience working in Linux environments with containerization tools such as Docker.

- Experience with synthetic data quality criteria, evaluation metrics, and the limitations of synthetic data.

- Background building or maintaining evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.

- Experience automating the generation, validation, mutation, or processing of structured datasets at scale.

- Ability to independently deliver technical projects in loosely defined problem spaces, with care for edge cases and data quality.

- Familiarity with reinforcement learning, agent workflows, or LLM post-training is useful.

### Compensation & Benefits

Base salary range: $100,000 to $170,000 USD annually. Visa sponsorship is available.

### Location

On-site in Singapore, Singapore.