Hiring nowSynthetic data and environment generation

Synthetic Data Engineer

Also called Synthetic Dataset Engineer · Data Generation Engineer

A Synthetic Data Engineer builds pipelines that generate artificial training and evaluation data, then proves it is useful. For a company, that means better models where real data is scarce, sensitive or too costly to collect.

What they do

Design generation recipes for a specific task, then build the pipelines that run them. That means choosing generative models or simulators, writing prompts and constraints, and adding validity checks at each stage. Filtering, deduplication and diversity checks sit alongside this. Each batch is tested by training or evaluating a model on it and measuring the effect. Gaps get found by looking at where models still fail, and the recipe is changed to cover them. Privacy constraints are handled in the design, not added afterwards. Everything is documented so someone else can rerun it.

What a strong hire delivers

A first synthetic dataset tied to a clear downstream task, with a written generation recipe the team can rerun. A validation suite that catches invalid, duplicated or leaked examples. Evidence from ablations showing what the data adds to model performance. A map of failure modes and how well the data covers them. A sensible plan for what to generate next and why.

How you can tell they are good

Ask them to walk through a dataset they generated and how they knew it helped. Strong people talk about ablations, baselines and failure coverage, not volume. They can explain how they caught bad samples, handled collapse in diversity, and avoided contaminating evaluation sets. Be wary of anyone who judges quality by eye or by size alone.

Why it is hard to fill

Nobody trains for this, so titles vary and the work hides inside research, data and platform teams. Good candidates are mixed: part modeller, part statistician, part test engineer. Many have built generation pipelines but cannot show them because the work is internal. Labs and data vendors compete for the same small group, so strong people are rarely on the market for long.

Where to find them

Look in applied research and data teams at labs and model providers, and in data vendors building generation products. Useful backgrounds include simulation engineering, generative modelling, statistics and test engineering. Some come from LLM pipeline roles focused on data quality, filtering and deduplication. Privacy engineers and people who built digital twins or synthetic test data in regulated industries are also worth a conversation.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Synthetic Data Engineer

  1. Describe a synthetic dataset you built. What task was it for, and how did you show it helped?

    Strong answers name the downstream task, a baseline and an ablation, and report the change honestly. They include cases where it did not help. Worry if success is described as dataset size or how realistic samples looked.

  2. How do you check that generated examples are valid before they reach training?

    Look for layered checks: programmatic validators, rule or simulator verification, model-based review and spot audits. They should know the cost of each. Worry about reliance on a single model judging its own output.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring a Synthetic Data Engineer to build the pipelines that generate data our models need and cannot easily collect. You will design generation recipes, check their validity and show, with evidence, what the data does for downstream performance. You will work closely with our research and evaluation teams.

What you will do

  • Design and build pipelines that generate task-specific synthetic data
  • Write generation recipes that others can rerun and audit
  • Build validators for correctness, duplication, leakage and privacy risk
  • Run experiments to measure downstream utility, diversity and failure coverage
  • Analyse model failures and target generation at the gaps
  • Keep provenance and documentation for every dataset released

What you will bring

  • Shipped at least one synthetic dataset used to train or evaluate a model
  • Experience with generative models or simulators in a production pipeline
  • Sound experimental design, including baselines and ablations
  • Practical filtering, deduplication and data quality work
  • Ability to explain quality trade-offs clearly to researchers and engineers
  • Comfort writing reliable, testable Python

Nice to have

  • Privacy engineering experience
  • Simulation or digital twin work
  • Background in statistics or test engineering

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Synthetic dataAdvancedGenerate test or training examples with validity controls
  • Experimental designAdvancedChoose comparisons that identify credible effects
  • Privacy engineeringWorkingLimit disclosure and unauthorised personal-data use
  • Simulation and digital twinsWorkingBuild useful virtual representations of environments

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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