Synthetic Data Research Engineer: Scale Realistic AI Tasks

Hud; Singapore; Teletrabalho; SGD 128 000 - 224 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Medical/dental/vision benefits (US &SG

Lunch/dinner for in-office employees

Equinox membership

401k

Commuter benefits

Health/wellness stipend (Singapore)

Unlimited tokens for AI tools

Office travel budget

Resumo da oferta

HUD is building reliable, fair and open AI data infrastructure with a growing team of researchers, engineers and operators. We seek Research Engineers to turn domain-specific workflows into synthetic training tasks that can scale and improve model capabilities.

Join a team of about 25 people working across San Francisco and Singapore with opportunities for remote candidates who can overlap hours with SF or Singapore time zones.

Qualificações

• Proficiency in Python, Docker, and Linux environments.
• Experience with synthetic data research methods — please elaborate in your application.
• Strong understanding of what “good synthetic data” means and its limitations.
• Built synthetic data pipelines end-to-end without a fully prescribed roadmap.
• Experience working on environments, evals, and benchmarks.

Responsabilidades

• Work with subject-matter experts to create synthetic tasks for training AI agents across domains.
• Design synthetic task generation methods that produce diverse, realistic, and learnable tasks.
• Build systems and tooling to mutate, validate, and improve synthetic tasks.
• Analyze model and agent performance on synthetic tasks to understand what they teach and where they fail.
• Develop metrics to quantify and understand synthetic task diversity, realism, learnability, etc.

Conhecimentos

Python

Linux environments

Synthetic data knowledge

Research prototyping

Ferramentas

Docker

Descrição da oferta de emprego

HUD is building reliable, fair and open AI data infrastructure with a growing team of researchers, engineers and operators. We seek Research Engineers to turn domain-specific workflows into synthetic training tasks that can scale and improve model capabilities.

Join a team of about 25 people working across San Francisco and Singapore with opportunities for remote candidates who can overlap hours with SF or Singapore time zones.