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-timeSingapore • SingaporeMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates proficiency in Python, Docker, and Linux environments while developing and analyzing synthetic data pipelines. Capable of designing diverse and realistic synthetic tasks and collaborating effectively in remote settings.

Highest-signal resume keywords

Proficiency In PythonExperience With Synthetic Data Research MethodsBuilding Synthetic Data Pipelines End To EndStrong Understanding Of Synthetic Data LimitationsClear Written And Spoken English

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Synthetic Task GenerationData AnalysisTask DesignMetrics DevelopmentPerformance Evaluation

Soft Skills

Detail-OrientedCollaborativeProblem-SolvingAdaptabilityCommunication

Tools & Technologies

DockerLinux

Industry Keywords

Synthetic DataAI TrainingRemote CollaborationTask ScoringBenchmarking

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 to understand what tasks teach and where they fail
Develop metrics for synthetic task diversity, realism, learnability and related qualities
Take synthetic data pipelines from early research through reliable systems
Turn domain-specific workflows into realistic, measurable and scalable training tasks
Build methods, tooling and metrics without a fully prescribed roadmap

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 overlap approximately 70–80% with San Francisco US Pacific or Singapore business hours for remote work
Technical aptitude and learning potential
Detail-oriented approach to identifying inconsistencies or 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

Benefits

Comp & perks

Remote work option for candidates in Singapore or US Pacific time zones
On-site work option in San Francisco or Singapore
Full-time employment
Opportunity to build a synthetic data pipeline from research through production
High ownership and work on unstructured problems
Collaboration with subject-matter experts and international teams