Hiring nowData engineering for AI

Training Data Infrastructure Engineer

Also called Pre-training Data Infrastructure Engineer

A Training Data Infrastructure Engineer builds the systems that feed datasets to model training at speed and scale. They keep every batch fast, repeatable and traceable, so training runs are not wasted on data problems.

What they do

They build and run the service that delivers datasets to training jobs. That means designing how data is sharded, shuffled and streamed to many machines without starving the accelerators. They write versioned manifests so every run can name exactly what it read. They add checks for corruption, duplicates and drift, and trace each batch back to its source. They profile slow loaders, fix bottlenecks in storage and networking, and work with researchers who need new data mixes quickly. When a run misbehaves, they help work out whether the data was the cause.

What a strong hire delivers

In the first months, a strong hire gives the training team a dataset service they can trust. Loading no longer limits training speed. Any run can be reproduced from its manifest. Integrity checks catch bad data before it reaches a job. Researchers can change data mixes without asking an engineer to hand-build a pipeline. Failures are easier to diagnose, and the team spends less time arguing about what the model actually saw.

How you can tell they are good

Ask them to walk through a data loading system they built and where it was slowest. Good candidates talk about measured bottlenecks, not guesses. Ask how they would reproduce a run from months earlier. They should describe manifests, immutable versions and recorded transforms. Ask what went wrong in production and what they changed afterwards. Vague answers about tools, with no numbers or failures, are a warning sign.

Why it is hard to fill

The title is rare and the work sits between disciplines. Many data engineers have never fed accelerators at scale, and many machine learning engineers have never built a dependable service. The strongest people often carry titles like backend engineer or data platform engineer, so keyword searches miss them. Frontier labs and larger model builders also hire from the same small group, which makes good candidates scarce and quick to move.

Where to find them

Look at data platform and backend teams that run large batch or streaming systems. Strong candidates come from data engineering, distributed systems and storage infrastructure. Some move across from machine learning platform teams or research engineering groups that built their own loaders. Companies training their own models, and those running heavy analytics at scale, are good places to start. Analytics engineers with deep pipeline experience can also stretch into this work.

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 Training Data Infrastructure Engineer

  1. Tell me about a data loading system you built. Where was it slowest, and how did you find out?

    A strong answer names a specific bottleneck, such as storage reads, serialisation or network limits, and how they profiled it. They describe the fix and the measured result. Worry if they only list tools or cannot say what was slow.

  2. How would you make a training run reproducible from its dataset alone?

    Look for versioned manifests, immutable data snapshots, recorded transforms, fixed shuffling seeds and clear lineage. They should note limits, such as nondeterminism elsewhere. Worry if they rely on folder names or say a table timestamp is enough.

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

Job description you can post

We train our own models and need an engineer to own how data reaches them. You will build the service that delivers fast, versioned, verifiable datasets to our training jobs, and work closely with researchers who depend on it every day.

What you will do

  • Build and run a dataset service that streams data to distributed training jobs
  • Design sharding, shuffling and caching to keep accelerators busy
  • Create versioned manifests so every run can be reproduced
  • Add integrity, quality and provenance checks to every delivered batch
  • Profile and fix loading bottlenecks across storage and network
  • Support researchers in changing and testing new data mixes
  • Monitor pipelines and lead the response when data issues appear

What you will bring

  • Built and operated data pipelines or data services at meaningful scale
  • Strong SQL and Python, shown in shipped work
  • Practical experience with distributed systems and batch processing
  • Experience with orchestration and data transformation tooling
  • Track record of measuring and improving throughput
  • Clear approach to data quality, lineage and versioning
  • Able to explain trade-offs to researchers and engineers

Nice to have

  • Experience feeding data to accelerator-based training
  • Familiarity with columnar and streaming data formats
  • Work alongside research teams on data mixes or evaluation

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

Skills that matter

  • Data pipelinesAdvancedBuild reliable ingestion and transformation systems
  • SQLAdvancedQuery and validate relational data
  • Data quality and provenanceWorkingTrack dataset origin, validity and transformations
  • Distributed systemsWorkingDesign reliable work across networked machines

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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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