Research Engineer, Synthetic Data

Pragmatike

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

Posted 11/12/2026full-timeRemote • California • United States, SingaporeMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates proficiency in Python, Docker, and Linux environments while developing and optimizing synthetic data pipelines. Capable of designing and analyzing synthetic tasks to enhance AI training across various domains.

Highest-signal resume keywords

Proficiency In PythonExperience With Synthetic Data Research MethodsBuilding Synthetic Data Pipelines End To EndClear Written And Spoken EnglishAbility To Identify Subtle Inconsistencies

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Synthetic Task GenerationData AnalysisTask DesignMetrics DevelopmentModel Performance Evaluation

Soft Skills

Collaboration Across Time ZonesProblem-Solving In Unstructured EnvironmentsReasoning From First Principles

Tools & Technologies

DockerLinux

Industry Keywords

Synthetic DataAI TrainingTask DiversityLearnabilityBenchmarking

Tech Stack

Tools & technologies

DockerLinuxPython

About the role

Key responsibilities & impact

Work with subject-matter experts to create synthetic tasks for training AI agents across professional and technical domains
Design synthetic task-generation methods producing diverse, realistic and learnable tasks
Build systems and tooling to mutate, validate and improve synthetic tasks
Analyse model and agent performance on synthetic tasks
Develop metrics for synthetic task diversity, realism, learnability and related qualities
Take synthetic data pipelines from early research through to reliable systems
Design tasks and systems that generate training data at scale
Turn domain-specific workflows into realistic, measurable and usable training tasks
Improve tasks based on model and agent failures

Requirements

What you’ll need

Proficiency in Python, Docker and Linux environments
Experience with synthetic data research methods
Strong understanding of what “good synthetic data” means and its limitations
Experience building synthetic data pipelines end to end
Experience working on environments, evals or benchmarks
Clear written and spoken English
Comfort collaborating remotely across time zones
Ability to identify subtle inconsistencies and edge cases in synthetic data
Ability to reason from first principles about task design, scoring and failure modes
Comfort working in unstructured problem spaces or early-stage environments
Ability to overlap approximately 70–80% with San Francisco (US Pacific) or Singapore business hours
Technical aptitude and learning potential; years of experience are not specified as a requirement

Benefits

Comp & perks

Full-time role
Remote work option for candidates in Singapore or US Pacific time zones
On-site work option in San Francisco or Singapore
Flexible daily schedule agreed during hiring
Equal employment opportunity and inclusive hiring process