BLACK OPS SOLUTIONS · IT Graduate IT Interview PackAU · 2026

AI / ML Engineer · graduate level · Australia

Graduate AI Engineer

Builds language-model features that have to work on real, messy inputs.

Job description · fictional employer

Graduate AI Engineer

Corella AI

Location
Sydney - hybrid, 3 days in office
Employment type
Full-time, permanent - 12-month graduate program
Salary
AUD $92,000 base + 12% superannuation + equity participation
Reports to
Head of Applied AI
Intake
January 2027 - applications close 31 August 2026

About us

Corella AI builds document intelligence for insurance and legal firms. Our customers send us claim files, policy schedules and discovery bundles - scanned, rotated, handwritten in the margins, forty years old - and expect structured answers with a citation for every one. We are 34 people, eleven of them engineers.

The team you would join

Applied AI is five engineers who sit between research and product. We do not train foundation models. We build the systems around them: retrieval, prompting, tool use, evaluation, guardrails and the unglamorous data plumbing that decides whether any of it works.

What you will do

  • Build and improve retrieval-augmented generation pipelines - chunking, embedding, retrieval, reranking, prompt assembly
  • Write evaluations before you write features. Every change ships with a measurement
  • Build labelled datasets from real customer documents, including doing some of the labelling yourself
  • Run structured error analysis: read failures one at a time and categorise them, do not skim aggregate scores
  • Serve models and pipelines behind FastAPI services, with sensible timeouts, retries and cost controls
  • Work directly with two named customers on accuracy problems, including reading their documents
  • Contribute to prompt and model version control - we treat prompts as code, reviewed and tested
  • Investigate a new technique each quarter and present whether it is worth adopting, with evidence

What we are looking for

  • A completed or in-progress bachelor or masters degree in computer science, data science, mathematics, statistics, engineering or a related quantitative discipline
  • Strong Python - you can write a class, handle exceptions properly, and use a virtual environment without help
  • Working understanding of core machine learning: train and test splits, overfitting, evaluation metrics, why accuracy is a poor metric on imbalanced data
  • Hands-on experience building something with a large language model API - a project, a hackathon, a thesis chapter
  • The instinct to measure rather than eyeball. If you cannot tell us how you would know your change helped, this role will be hard
  • Ability to read a paper or a model card and extract what is actually claimed
  • Full Australian working rights

Nice to have

  • PyTorch or JAX beyond a tutorial
  • Any experience with vector search, embeddings or reranking models
  • Exposure to evaluation frameworks, or to writing your own
  • Data engineering fundamentals - SQL, pandas, working with awkward file formats
  • Any experience with document processing, OCR or information extraction
  • A public writeup of something you tried that did not work

Our stack

Python 3.12PyTorchHugging Face TransformersFastAPIPostgreSQL + pgvectorAnthropic and OpenAI APIsAWS BedrockMLflowDockerPrefectWeights and Biases

What the program gives you

  • A first project with a real customer and a real accuracy target in week three
  • One day a fortnight for research reading, with a paper discussion the team actually attends
  • Compute budget for experiments, and no requirement to justify a failed one
  • Mentoring from an engineer who has shipped model-backed features to production
  • Conference or workshop attendance annually, and support to publish or present internal work

How the process runs

  1. 1

    Application

    CV plus a link to something you built with a model, and two paragraphs on what did not work about it.

  2. 2

    Take-home

    Four hours, paid. A small extraction task on messy documents, with a held-out test set you do not see.

  3. 3

    Technical interview

    75 minutes - walkthrough of the take-home, ML fundamentals, and how you would improve your own result.

  4. 4

    Applied AI interview

    60 minutes - retrieval, evaluation design, failure analysis, cost and latency trade-offs.

  5. 5

    Team and values interview

    45 minutes - communication, intellectual honesty, working with non-technical customers.

  6. 6

    Offer

    Within a week of the final stage.

We are more interested in how you think about being wrong than in how many models you can name. Candidates who bring a project where they measured something, found it disappointing, and worked out why do consistently well here. Reasonable adjustments are available at any stage.

Example CV · fictional candidate

Wei Zhang

Written to the job description on the previous tab. Notes on the right explain each choice.

Wei Zhang

Graduate AI / Machine Learning Engineer

Sydney NSW · 0400 000 000 · w.zhang@example.com · github.com/weizhang-ml · weizhang.example.com (writeups)

Professional summary

Advanced computing honours graduate focused on applied language-model systems. Honours thesis on retrieval quality in domain-specific question answering, plus a summer building an evaluation harness that a commercial team still uses. I measure before and after every change, and I write up the things that did not work.

Technical skills
Languages
Python (primary), SQL, C++ (coursework), JavaScript (basic)
ML and DL
PyTorch, scikit-learn, Hugging Face Transformers, sentence-transformers
LLM systems
RAG pipelines, pgvector, FAISS, reranking, structured output, evaluation harnesses, prompt versioning
Data
pandas, NumPy, PostgreSQL, Polars (basic), PDF and OCR tooling
MLOps
MLflow, Weights and Biases, Docker, FastAPI, GitHub Actions
Maths
Linear algebra, probability and statistics, optimisation (university level)
Education
Bachelor of Advanced Computing (Honours Class I), majoring in Machine Learning and Data Science
Feb 2023 - Dec 2026

University of Sydney

  • WAM 82. Honours thesis mark 89
  • Thesis: retrieval quality as the limiting factor in domain-specific question answering. Built a 480-question labelled evaluation set over Australian tenancy legislation and showed reranking recovered 21 points of answer accuracy where prompt changes recovered 4
  • Relevant units: Statistical Machine Learning (HD), Deep Learning (HD), Natural Language Processing (HD), Optimisation (D), Database Systems (D)
Experience
Machine Learning Intern
Nov 2025 - Feb 2026 (14 weeks)

Wrenfield Analytics, Sydney

  • Built the team's first automated evaluation harness for an internal document classifier: 340 labelled examples, per-class metrics, regression checks in CI. Still in use
  • Ran error analysis on 200 misclassifications and found 38 were mislabelled in the ground truth, which changed the reported baseline by 4 points
  • Reduced average inference cost per document by 46% by routing short documents to a smaller model and only escalating on low confidence
  • Wrote the internal note explaining why a proposed fine-tune was not worth doing, which the team accepted
Undergraduate Research Assistant (casual, 10 hrs/week)
Mar 2025 - Nov 2025

USyd School of Computer Science

  • Prepared and cleaned a 1.2 million document corpus for a supervisor's NLP project, including deduplication that removed 14% of near-duplicates
  • Reproduced results from two published papers and documented where the reported numbers could not be reproduced
Mathematics Tutor (casual)
Feb 2023 - Dec 2025

Private and school-based tutoring

  • Taught HSC Extension 1 and 2 mathematics to 12 students across three years
  • Direct practice at explaining technical ideas to people who do not yet have the vocabulary
Projects
Tenancy QA - grounded question answering over legislation
Python, pgvector, sentence-transformers, cross-encoder reranking, FastAPI
  • Hybrid retrieval with reranking over 2,400 sections of state tenancy legislation, every answer citing a section number
  • Published the 480-question evaluation set and a writeup of three approaches that made results worse
  • github.com/weizhang-ml/tenancy-qa
Receipt extraction from photographs
PyTorch, OCR, layout-aware extraction
  • End-to-end extraction of merchant, date, total and line items from phone photos, tested on 600 self-collected receipts
  • Documented that accuracy fell from 91% to 63% on receipts printed on thermal paper more than a year old, and why
Writeups
Technical writing
  • Nine posts on evaluation design and retrieval failures, including 'Three RAG improvements that made my system worse'
Leadership and activities
  • Co-organiser, USyd Machine Learning Society reading group, 2025 - 2026. Ran fortnightly paper discussions with 20 to 30 attendees
  • Kaggle - top 8% in a document classification competition, 2025. Writeup published
Certifications
  • DeepLearning.AI Natural Language Processing Specialisation, 2024
Referees

Available on request.