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:

You’ll have the autonomy to choose the methods, tools, and approaches that work best for you, with success measured against the objectives outlined below.

• 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.

Our current tools include Python, SQL, Databricks (Spark, MLflow), open-source ML and deep learning frameworks, LLM APIs and cloud infrastructure. We adopt new tools when they serve the problem.

What Success Looks Like:

We measure success by results, not activity:

• 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:

We are a small team and intend to stay that way, so every hire raises the bar. Your skills earn you an interview; the way you work earns you the role.

• 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: hypothesis testing, A/B tests, causal reasoning, and turning analysis into business decisions.

• Strong command of model evaluation: experimental design, appropriate metrics, validation strategy and avoiding leakage.

• Strong Python and SQL, with experience in common ML and deep learning frameworks such as scikit-learn, PyTorch or TensorFlow.

• Solid software engineering: production services and APIs, automated testing, CI/CD and cloud platforms (AWS, Azure or GCP).

• Regular use of AI coding tools, with a clear understanding of their limitations.

• Excellent written and spoken English, with the ability to explain technical work to non-technical audiences.

Nice to have Skills and Experiences:

• Experience in financial services, such as lending, credit risk or fraud.

• Deep learning for NLP, computer vision, time-series or other sequential data.

• LLM application patterns: retrieval-augmented generation, agents, tool use and LLM evaluation.

• MLOps experience: model registries, monitoring, MLflow and infrastructure as code such as Terraform. Large-scale data processing with Spark or Databricks.

• Data visualisation and BI tools, and experience presenting insights to senior stakeholders.

• A project you built on your own initiative that others now rely on.

What’s in it for you:

MONEYME’s employees and culture are core to who we are. We know that without a high-performing and engaged Team MONEYME, we will not achieve our ambitious goals for the future. We are proud to offer a collaborative and fun work environment.

Some of the benefits & perks we offer for all our employees in Manila are:

• HMO on Day 1 + 1 free dependent

• 15 days of vacation leaves and 15 days of sick leave

• 1 birthday leave

• Health and wellbeing initiatives like weekly sports activities and MONEYME Olympics

• Fun filled company activities - summer outings, team building, team lunch or dinner, Halloween event, year-end party and so much more!

• Complimentary snacks in the office

• MONEYME Merchandise - hoodie, T-shirt, tumbler, notebook, and id lace

• Quarter champion awards & reward trips

At MONEYME we believe in rewarding hard work. When the business is winning, so are you and we’re always investing in our employees to lead new projects and develop people’s careers. We have quarterly awards, events, bonuses and more.

MONEYME Limited is an equal opportunity employer and we value diversity, equity, and inclusion. We are committed to creating a diverse and inclusive workplace and encourage applicants from all backgrounds to apply. We believe that the unique contribution of our employees is a key driver of our success. We stand together – our diversity and inclusion give us an edge.