Senior AI Engineer

About MONEYME:

MONEYME is a founder‑led digital lender and Certified B Corporation ™. We challenge the traditional ways of credit and simplify the borrowing experience with digital‑first experiences that meet the needs of modern consumers. We offer a range of fast, flexible, and competitively priced products that span our customers’ credit lifecycle, including personal loans, credit cards, and car loans. We deliver unrivalled customer experiences powered by smart technology, speed and efficiency. We are for ambitious Australians that expect more from life and the companies they engage with. We uphold a strong ethos of sustainability and hold ourselves accountable to the high standards of the B Corp movement. Our culture is energetic and driven, and we continually challenge the status quo… we’re nothing like your traditional finance institution. We have recently been certified as a B Corp and a Great Place To Work . We’re proud to have built a culture where people feel heard, cared for, and empowered to push boundaries. We wouldn’t be able to continually improve and grow as a company without our diverse and exceptional team.

What we are looking for:

MONEYME is hiring a Senior AI Engineer whose scope goes beyond building AI software. Alongside designing, building and running AI systems in production, you will bring data science capabilities: analysing data, running experiments and turning evidence into decisions. We define this role by the objectives it must achieve, not by a fixed list of responsibilities. How you get there is up to you.

This is an outcome‑driven role

You will be given business objectives, not a task list, and you decide how to meet them: analysing the data, framing the problem, choosing whether and which AI to use, then building, deploying and measuring the solution. The work covers lending, risk, customer operations and beyond, and spans machine learning, deep learning and LLM applications. You will work closely with the Head of Data Intelligence and a lead AI engineer. We are a small team: each person owns a significant area and uses AI tools to move quickly.

Senior AI Engineer Objectives:

• Better decisions. Improve the quality and speed of business decisions through data and AI, with gains we can measure.
• Less manual work. Remove repetitive work from our operations by taking AI solutions all the way into production.
• Trustworthy AI. Keep the AI systems in your area accurate, reliable and cost‑effective, so the business can depend on them.
• Evidence over opinion. Back every significant change with sound analysis or experiments, so we know what works and why.
• The next opportunity. Find where data and AI can create the most value next, and make the case for it.

What Success Looks Like:

• Measurable impact. Your analysis and the AI systems you build improve outcomes the business can measure, such as decision quality, customer experience, cost or manual effort.
• Production quality. Your systems are reliable, monitored and well documented, and issues are caught and fixed before users are affected.
• Trusted results. Your evaluation is rigorous enough that the business can act on your numbers with confidence.
• Ownership. You become the go‑to person for the data and AI systems in your area and set their direction.
• Raising the bar. You bring in better methods across machine learning, deep learning and LLMs, and share what you learn with the team.

How We Work:

• Ownership. You take responsibility for outcomes, not just tasks, and follow your work through to production and beyond.
• Rigour. You check outputs, question results that look too good, and test against real and edge cases before you ship.
• Root‑cause thinking. When something breaks, you find the underlying cause, put safeguards in place and share what you learned.
• Initiative. You spot what is slow, wrong or missing, and you propose and build the solution without waiting to be asked.
• Sound judgment. You make decisions with incomplete information, state your assumptions and revisit them as evidence comes in.
• Clear communication. You bring a point of view, challenge constructively and explain complex ideas simply.
• Curiosity. You keep up with a fast‑moving field and adopt new methods when they genuinely improve results.

To be successful for this role the following skills and experience is required:

• Must have at least 4+ years in data science, machine learning or AI engineering, including models or AI systems you took to production.
• Hands‑on depth in at least one of machine learning, deep learning or LLM applications, and the interest to work across all three.
• Strong grounding in statistics and experimentation: hypothesi