Quant & AI Engineer

We are seeking a Quant & AI Engineer to join the QMAT (Quant and Macro, Alternative Data and Trading) team with a passion for financial markets and investing, joining our Global Equities business. We expect 3-6 years of relevant industry work experience (or equivalent). The role will be Sydney based.

Strong academics in Software Engineering, Computer Science, or Data Science are ideal. Studies in Finance/Quant Finance, Statistics, Mathematics, Physics, or Actuarial Science are valued but not essential. Advanced degree (MSc/PhD) or equivalent industry experience is welcome. Hands-on AI/ML systems experience is highly desirable.

Near-term Responsibilities:

Quant System Implementation

• Collaborate with portfolio managers, analysts, traders, and data engineers to productionise quant signals and workflows that complement both systematic and discretionary fundamental investing processes.
• Take ownership of robustness, including monitoring, backtest hygiene, out-of-sample validation, and guardrails to prevent data leakage or overfitting.
• Contribute to a systematic research platform (factor library, feature store, research notebooks, experiment tracking).

AI Systems Integration

• In partnership with the fundamental sector teams, implement and integrate LLM and ML tooling (e.g., retrieval/summarisation, idea generation, research copilots) to enhance equity research productivity and workflows. Requires the ability to abstract Excel/DCF valuation models into structured, reusable drivers – enabling integration into research platforms and AI workflows.
• Stand up secure, compliant AI workflows (access controls, NPI handling, model risk checks, auditability), partnering with operations and IT teams.
• Track and benchmark emerging AI capabilities; convert the most promising into practical, measurable productivity gains or research insights.

Software Development & Data Foundations

• In partnership with the operations and data engineering team, design, write, and maintain reliable, testable Python code (R optional) for investment research tooling, data processing, and strategy implementation.
• Assist with the building and operating of data pipelines (batch/stream) across SQL, REST APIs, AWS S3, Azure Blob Storage, and Snowflake. Familiarity with tools such as Airflow, Docker, and Git preferred.
• Ensure data quality, lineage, reproducibility, and cost-aware cloud usage across environments.

Growth Path(s):

AI-Driven Investment Innovation

• Contribute to the expansion of AI-enabled investment processes across research, trading, and risk management.
• You will contribute to implementing the firm's AI strategy roadmap, including vendor partnerships, model governance, and in-house research.
• Potential to grow into a senior role responsible for embedding AI as a durable edge in Antipodes' investment process.

Alpha Signal Research & Integration

• Research and test new alpha signals (including traditional and alternative data sources) and advanced feature engineering; assessing economic/causal intuition and alpha potential.
• You will integrate these validated signals into existing processes with clear attribution.

Quantitative Research & Strategy Development

• Design and iterate systematic equity strategies with rigorous backtesting, risk controls, and implementation realism (liquidity, costs, borrow, capacity).
• Enhance simulation frameworks (event-time tests, walk-forward, cross-validation) and contribute to shared research tooling.

Collaboration & Communication

• Work closely with CIO, PMs, sector teams, traders, and data engineers to align research with investment goals.
• Produce concise write-ups and presentations on methods, assumptions, limitations, and business impact; contribute to team knowledge sharing.

About you:

• Outcome-oriented problem solver who prioritises business impact and knows when "good enough now" beats "perfect later."
• Fluent in Python (pandas/NumPy); experience with scikit-learn and/or PyTorch/TensorFlow is a plus. Comfortable with SQL and cloud data workflows.
• Mathematically minded (linear algebra, probability, statistics, time series); pragmatic about modelling assumptions.
• Strong interest in valuation mechanics (e.g., DCF, relative value, cash flow drivers) and how these can be systematised and enhanced through AI and quantitative methods.
• Data detective who is rigorous about data quality and enjoys turning messy, high-dimensional data into tractable features.
• Clear communicator who can explain complex ideas simply to non-quants and invite constructive challenge.
• Builder's mindset: version control, tests, code review, documentation; with curiosity to prototype, measure, and iterate.

We value:

• Natural curiosity about the events and trends that shape markets.
• First-principles thinking from hypothesis to measured recommendation.
• Creative, independent, rational judgement under uncertainty.
• Patience, persistence, resilience in the face of noisy data and shifting regimes.
• Selfless team players who raise the bar for others.

How to apply:

Please email both and with "QMAT Job Application" as the email subject and attach a Curriculum Vitae (pdf preferred).