--- title: 'Data Engineer at Maincode' canonical: 'https://feeny.ai/job/data-engineer-maincode-australia-5e07rgq3nak0' type: 'job' last_seen: '2026-10-16' --- # Data Engineer at Maincode - **Company:** Maincode - **Location:** Australia - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-10-10 - **Last confirmed live:** 2026-10-16 - **Apply:** https://jobs.ashbyhq.com/maincode/82a456a1-d4c5-48af-a80a-581fbff88e2b ## Job description ## About Maincode Maincode makes Matilda, an Australian-made AI stack we own end to end, from tin to token. We own and operate our own infrastructure here in Australia, we train and maintain our own frontier models, and we run multiple surfaces on top of them, including chat, coding and API. Everything we ship is built on capability we control ourselves. We are hiring someone who cares about that. You should be passionate about Australian-made technology, a builder first, and keen to execute at the frontier of data engineering and model training. ## The role A frontier model is only as capable as the data it learns from, and every model carries the worldview of the data it was trained on. We did this for Australia with Matilda’s voice. Now we are building the capability to do it for any country or market: training open weights models on custom datasets that reflect the cultural, regulatory, governance, and ideological requirements of the people who will use them. You will be the hands on engineer who builds the system that makes those datasets. You will own the pipeline end to end, from web scale crawling of hard to reach, country specific sources through to filtering, deduplication, quality control, and the agent driven pipelines that turn raw material into training sets. We need someone who moves fast, writes code every day, and knows how to use AI agents to multiply what a small team can produce without letting quality slip. ## What you will do - Build, run, and scale crawlers and scrapers for web scale collection, including specialized crawlers for high value, hard to reach sources such as legislation, regulator guidance, government publications, local media, and community forums. - Develop robust pipelines for extraction, filtering, cleaning, deduplication, and formatting to ensure the highest quality inputs for model training. - Build agentic pipelines where AI agents generate, annotate, critique, and verify data at scale, with the evaluation and spot checks needed to trust what they produce. - Turn a market’s requirements, from its laws and regulators to its cultural norms and governance expectations, into dataset specifications, data mixtures, and evaluations we can measure against. - Work directly with our core training team to iterate on data mixtures, evaluate data quality, and respond to model performance feedback. - Track provenance, licensing, privacy, and security for every dataset you ship. ## What you will bring - A track record of building, scaling, and running large scale web crawling or scraping systems in production. - Strong engineering in Python and at least one of Go, Rust, or C++, with real experience in distributed data processing (Spark, Ray, Beam, or similar) and petabyte scale storage. - Hands on knowledge of the hard parts of crawling: JavaScript heavy sites, headless browsers, rate limiting, robots.txt, and access and licensing constraints. - Experience using LLMs and AI agents inside data pipelines for classification, extraction, synthetic data generation, or quality scoring, and a clear sense of where they fail. - A bias for shipping. You get a working pipeline running in days, then make it reliable. - Experience leading the technical direction of a project and bringing other engineers along with you. Highly regarded - Direct experience building pre-training or post-training datasets for large language models, including SFT, preference, or reinforcement learning data. - Experience with multilingual, regional, or culturally specific data, or with evaluating models against local norms and regulation. - Experience fine-tuning or post-training open weights models. - Open source contributions to crawling, data processing, or dataset tooling. - A strong network within the Australian technology, academic, or media ecosystem to facilitate unique data partnerships. Eligibility - Must have the right to work in Australia. - Willingness to undergo background checks as required. Details - Location: Melbourne, VIC (or Hybrid/On-Site across Australia). - Type: Full time, permanent. - Remuneration: Negotiable based on experience, plus super and equity. Who thrives here Engineers who understand that high quality data is the true moat of any frontier model, and that it is also where a model’s values come from. If you care about Australian-made technology and you would rather spend your week shipping crawlers, tuning agent pipelines, and reading samples of your own data than sitting in steering committee meetings, you will do well here. ## About Maincode ## Company Overview - **One-liner**: Maincode is an Australian AI research and product company building Matilda, an intelligent assistant that understands context, learns your workflow, and helps you move faster and more safely. - **Entity Type**: Private (Bootstrapped – no external funding rounds disclosed) - **Headquarters**: Melbourne, Australia - **Founded**: Not publicly disclosed (recent high-growth signals suggest founding around 2024–2025) - **Founders**: Not publicly disclosed (Dave L. is listed as Chief Coder; founder status unclear) ## Core Business - **Primary industries**: AI research, applied intelligence, product development for enterprise and knowledge workers. - **Target customers**: B2B (teams and individuals needing a nuanced, safe AI assistant), researchers, and developers. - **Mission**: “AI that earns trust in the real world, to move humans forward.” Focus on safe, transparent, and helpful AI systems. ## Products & Services - **Matilda** (flagship product, private beta 2026): AI assistant with deep contextual understanding across long conversations, multi-step reasoning (inspectable steps), proactive but permission-asking behavior, learning of user workflows over time. Built and hosted in Australia, aligned with Australian privacy standards. - **Maincoder** (open-source 1B parameter model, released Dec 2025): Code generation and transformation model optimized for low latency and cost. - **AI Research Residency** (3–6 month paid program): For late-stage PhD students and early-career researchers. Provides access to Maincode’s GPU cluster, infrastructure, and direct collaboration with the core team; aims to publish at top-tier venues. - **Inference infrastructure (MC-2 cluster)**: High‑availability, sub-200ms global latency inference using custom batching, model sharding, dynamic routing (CUDA, Kubernetes, Terraform, GCP, vLLM, AMD Instinct GPUs). - **Context Engine & Action Layer**: Proprietary frameworks for long-context reasoning and agentic tool-use/sandboxed execution (turning Matilda from chatbot to agent). - **Eval Suite**: Internal evaluation framework for model capability, safety alignment, task completion (10K+ scenarios, automated regression). ## Market Standing - **Valuation / Market Cap**: Not disclosed. - **Key Metric**: Total funding not disclosed (appears bootstrapped). Headcount: 17 employees (+360% YoY). Monthly website visits: ~14,071 (+116.3% monthly, +56,184% yearly). - **Notable Investors/Partners**: - Demonstrating first agentic payment transaction in Australia with **Mastercard** at the Australian Open 2026. - Benchmarking study with **Heidi Health** (cloud vs on‑premise inference). - **Growth Signals**: - 10 active job postings (quarterly increase +42.9%, yearly +150%). - LinkedIn followers: 4,468 (+315.6% yearly). - Traffic growth (56,184% yearly) and strong inbound interest from US (72%) and Australia (28%). - Partnership with Mastercard for a live agentic payment demo signals enterprise traction. ## Competitive Advantages - **Australian privacy by design**: Infrastructure and model behaviour aligned with Australian standards; transparency and user control emphasized. - **Research‑to‑production pipeline**: Tight integration between frontier research and a shipped product (Matilda). The research residency directly feeds into product improvements. - **Agentic safety**: Multi-step reasoning with visible steps, permission‑asking before action, and sandboxed execution – differentiating from black‑box chatbots. - **Own GPU hardware**: MC-2 cluster (AMD Instinct) allows full control over inference latency and cost. - **Talent magnet**: Small, high‑agency team attracts PhD‑level researchers with promise of publications and access to SOTA compute. ## Strategic Focus - Scale Matilda’s user base (currently private beta) and move toward general availability. - Deepen research in safety & alignment, long‑context reasoning, multimodal perception, agent orchestration, and model efficiency. - Expand enterprise partnerships (evidenced by Mastercard, Heidi Health). - Continue growing the team across engineering and research (10 open roles across infrastructure, product, and research). ## Why Work Here - **Culture**: “Small team doing hard things.” High ownership, move fast, ship without permission. Values: ownership, gradient (fast learning), range, speed, taste, humility. No ego, mission‑first. - **Work environment**: On‑site in Melbourne, Australia (some hybrid/remote possible with visits). Emphasis on craft and shipping. - **Research perks**: For researchers – paid residency, ability to publish at top venues (NeurIPS, etc.), access to dedicated GPU cluster, direct line to production. - **Engineering focus**: Modern tech stack (Python, PyTorch, CUDA, Kubernetes, Terraform, GCP); engineers work on inference infrastructure, model training, safety evals, and product features. - **Growth opportunity**: From 17 people to planned expansion; early employees can shape the direction of the product and research agenda. ## Sources 1. [maincode.com](https://maincode.com/) – Company website, product description, culture page 2. [LinkedIn – Maincode](https://au.linkedin.com/company/maincodehq) – Headcount, growth metrics, partner announcements 3. [Built In – Maincode Careers](https://builtin.com/company/maincode) – Culture summary and active job listings 4. [AI Directory – Maincode Pty Ltd](https://aidirectory.industry.gov.au/organisation/maincode-pty-ltd) – Business area and location verification 5. [maincode.com/platform](https://maincode.com/platform) – Detailed product and research area descriptions ## Other roles at Maincode - [Forward Deployed Engineer (Enterprise)](https://feeny.ai/job/forward-deployed-engineer-enterprise-maincode-melbourne-qxahp9fm49vh) — Melbourne, Australia - [Account Director](https://feeny.ai/job/account-director-maincode-australia-fn94ranbcqrg) — Australia - [Graduate Software Engineer](https://feeny.ai/job/graduate-software-engineer-maincode-melbourne-zb15wrcbrp5d) — Melbourne, Australia - [Investment Associate](https://feeny.ai/job/investment-associate-maincode-melbourne-wqfbfym36f7s) — Melbourne, Australia - [Software Engineer](https://feeny.ai/job/software-engineer-maincode-melbourne-8pdhztj4jdk1) — Melbourne, Australia - [Digital Marketing Specialist](https://feeny.ai/job/digital-marketing-specialist-maincode-melbourne-aesffjn2mm6h) — Melbourne, Australia - [Talent Sourcer](https://feeny.ai/job/talent-sourcer-maincode-australia-1y96x7ezwa0f) — Australia - [Behavioural Researcher](https://feeny.ai/job/behavioural-researcher-maincode-melbourne-a14qj2zc6b6c) — Melbourne, Australia - [Forward-Deployed AI Engineer, Justice and Community Safety](https://feeny.ai/job/forward-deployed-ai-engineer-justice-and-community-safety-maincode-melbourne-72q4z3ny2v3d) — Melbourne, Australia - [Commercial Counsel (AI & Technology)](https://feeny.ai/job/commercial-counsel-ai-technology-maincode-melbourne-axbjnskc6y8h) — Melbourne, Australia