Research Engineer, Synthetic Data (Pacific Time)

Pragmatike | Artificial Intelligence

33m ago

Worldwide Remote

Full Time

San Francisco, Singapore

Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones)
Start Date: ASAP
Languages: English
Team: Engineering

About the Role

Our client is a fast-growing AI company building reliable, fair and open infrastructure for AI training data. Their marketplace helps the people who create data get value for it, and gives AI labs trustworthy data to train on. It is already used by startups, Fortune 500 companies and frontier labs, and the company is backed by leading venture investors, including Y Combinator. They are scaling quickly to meet strong demand.

We are looking for a Research Engineer, Synthetic Data to build the synthetic data pipeline. You will turn domain-specific workflows into synthetic training tasks that are realistic enough to teach useful behaviour, structured enough to generate at scale, and difficult enough to expand model capabilities.

This is a hands-on research engineering seat, not a commercial or privacy-systems role. You will design how synthetic tasks are created, mutated and validated, and you will analyse what those tasks actually teach models and agents.

This is a full-time role, starting ASAP. Offices are in San Francisco and Singapore; remote candidates are welcome if their working hours can overlap about 70–80% with either San Francisco (US Pacific) or Singapore business hours. The exact daily schedule will be agreed during hiring.

What You'll Do

Work with subject-matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains.
Design synthetic task-generation methods that produce 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 the tasks are teaching and where they fail.
Develop metrics to quantify and understand synthetic task diversity, realism, learnability and related qualities.
Take synthetic data pipelines from early research through to reliable systems, including in areas where the roadmap is not fully prescribed.

What We're Looking For

Proficiency in Python, Docker and Linux environments.
Experience with synthetic data research methods — please be ready to describe this in your application.
A strong understanding of what “good synthetic data” means, and of its limitations.
Experience building synthetic data pipelines end to end, including where there was no fully prescribed roadmap.
Experience working on environments, evals or benchmarks.
Clear written and spoken English, and comfort collaborating remotely across time zones.
Strong candidates may also be detail-oriented enough to catch subtle inconsistencies or edge cases in synthetic data; able to reason from first principles about task design, scoring and failure modes; and comfortable in unstructured problem spaces or early-stage environments.
Technical aptitude and learning potential matter more than years of experience. Motivated candidates are encouraged to apply even if they don't meet every point above.

Why This Role Matters for You

Build the synthetic data pipeline: You will design the tasks and systems that generate training data at scale, not only run experiments on someone else's pipeline.
Research that has to work in production: Turn messy, domain-specific workflows into tasks that are realistic, measurable and usable for training agents.
See what the data actually teaches: Analyse model and agent performance, and improve the tasks when they fail.
High ownership, unstructured problems: You will build without a fully prescribed roadmap, from method design through tooling and metrics.
San Francisco, Singapore, or remote: Work on-site in San Francisco or Singapore, or remotely, with hours that overlap about 70–80% with either US Pacific or Singapore business hours. The exact daily schedule will be agreed during hiring.

Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.

Required Skills

pythondockerlinux

Required Languages

🇬🇧 English

About the Role

What You'll Do

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

Design synthetic task-generation methods that produce 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 the tasks are teaching and where they fail.

Develop metrics to quantify and understand synthetic task diversity, realism, learnability and related qualities.

Take synthetic data pipelines from early research through to reliable systems, including in areas where the roadmap is not fully prescribed.

What We're Looking For

Proficiency in Python, Docker and Linux environments.

Experience with synthetic data research methods — please be ready to describe this in your application.

A strong understanding of what “good synthetic data” means, and of its limitations.

Experience building synthetic data pipelines end to end, including where there was no fully prescribed roadmap.

Experience working on environments, evals or benchmarks.

Clear written and spoken English, and comfort collaborating remotely across time zones.

Strong candidates may also be detail-oriented enough to catch subtle inconsistencies or edge cases in synthetic data; able to reason from first principles about task design, scoring and failure modes; and comfortable in unstructured problem spaces or early-stage environments.

Technical aptitude and learning potential matter more than years of experience. Motivated candidates are encouraged to apply even if they don't meet every point above.

Why This Role Matters for You

Build the synthetic data pipeline: You will design the tasks and systems that generate training data at scale, not only run experiments on someone else's pipeline.

Research that has to work in production: Turn messy, domain-specific workflows into tasks that are realistic, measurable and usable for training agents.

See what the data actually teaches: Analyse model and agent performance, and improve the tasks when they fail.

High ownership, unstructured problems: You will build without a fully prescribed roadmap, from method design through tooling and metrics.

San Francisco, Singapore, or remote: Work on-site in San Francisco or Singapore, or remotely, with hours that overlap about 70–80% with either US Pacific or Singapore business hours. The exact daily schedule will be agreed during hiring.