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
