Senior Data Engineer

Overview

Maincode is building sovereign AI models in Australia. We are training foundation models from scratch, designing new reasoning architectures, and deploying them on state-of-the-art GPU clusters. Our models are built on datasets we create ourselves, curated, cleaned, and engineered for performance at scale. This is not buying off-the-shelf corpora or scraping without thought. This is building world-class datasets from the ground up.

As a Senior Data Engineer, you will lead the design and construction of these datasets. You will work hands-on to source, clean, transform, and structure massive amounts of raw data into training-ready form. You will design the architecture that powers data ingestion, validation, and storage for multi-terabyte to petabyte-scale AI training. You will collaborate with AI Researchers and Engineers to ensure every byte is high quality, relevant, and optimised for training cutting-edge large language models and other architectures.

This is a deep technical role. You will be writing code, building pipelines, defining schemas, and debugging unusual data edge cases at scale. You will think like both a data scientist and a systems engineer, designing for correctness, scalability, and future proofing. If you want to build the datasets that power sovereign AI from first principles, this is your team.

What you'll do

- Design and build large-scale data ingestion and curation pipelines for AI training datasets

- Source, filter, and process diverse data types including text, structured data, code, and multimodal, from raw form to model-ready format

- Implement robust quality control and validation systems to ensure dataset integrity, relevance, and ethical compliance

- Architect storage and retrieval systems optimised for distributed training at scale

- Build tooling to track dataset lineage, reproducibility, and metadata at all stages of the pipeline

- Work closely with AI Researchers to align datasets with evolving model architectures and training objectives

- Collaborate with DevOps and ML engineers to integrate data systems into large-scale training workflows

- Continuously improve ingestion speed, preprocessing efficiency, and data freshness for iterative training cycles

Who you are

- Passionate about building world-class datasets for AI training from raw source to training-ready

- Experienced in Python and data engineering frameworks such as Apache Spark, Ray, or Dask

- Skilled in working with distributed data storage and processing systems such as S3, HDFS, or cloud object storage

- Strong understanding of data quality, validation, and reproducibility in large-scale ML workflows

- Familiar with ML frameworks like PyTorch or JAX, and how data pipelines interact with them

- Comfortable working with multi-terabyte or larger datasets

- Hands-on and pragmatic, you like solving real data problems with code and automation

- Motivated to help build sovereign AI capability in Australia

Why Maincode

We are a small team building some of the most advanced AI systems in Australia. We create new foundation models from scratch, not just fine-tune existing ones, and we build the datasets they run on from the ground up.

We operate our own GPU clusters, run large-scale training, and integrate research and engineering closely to push the frontier of what is possible.

You will be surrounded by people who:

- Care deeply about data quality and architecture, not just volume

- Build systems that scale reliably and repeatably

- Take pride in learning, experimenting, and shipping

- Want to help Australia build independent, world-class AI systems

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