Mid-Senior Machine Learning Engineer

About Ferocia

We're the team behind Up, but under the hood, we're Ferocia - a passionate tech company driven by financial inclusion. Since 2011, we've been crafting innovative financial tools, starting with the digital platform for Bendigo Bank. We believe technology can empower everyone, from the advantaged to the disadvantaged, which is why Up was born.

Now, as part of the Bendigo and Adelaide Bank family, we combine the agility of a small company with the reach and stability of a major player. Together, we're carbon neutral, community-focused, and dedicated to high standards of corporate governance. Our mission? To leverage technology to help Australians move from financial stress and anxiety to a place of confidence and empowerment.

Want to join us? We'd love to hear from you!

The role

We’re looking for a mid-to-senior level Machine Learning Engineer to build, deploy, and scale the models and systems that power Up. This includes creating intelligent features in the Up app, as well as developing systems to automate internal processes, increase the efficiency of customer support, and forecast business impact. You’ll be joining a small team of Data and ML Engineers, tasked not just with using our data platform, but expanding its capabilities to encompass ML training, inferencing, and model deployment. Our focus is on using our rich data to build intelligent systems that will improve the financial lives of our customers and enhance how we serve them.

This is a rare opportunity to join our small but growing Data & ML team and have an outsized impact on the future of Up, Australia’s highest-rated banking app. Backed by Bendigo Bank, we operate with the speed of a startup and the scale of an established institution.

Impact

The role’s impact goes far beyond training models in a notebook. You’ll own and automate the end-to-end lifecycle of ML models, taking them from initial concept through to production systems that serve millions of customers:

• You'll play a key role in moving our ML capabilities forward, helping us transition from infrequent, manual training to fully automated, continuously monitored systems.
• You’ll be pivotal in the development of our MLOps practices and tooling, ensuring our models are robust, reliable, and easy to maintain.
• You will be at the forefront of our natural language processing initiatives, deriving value from unstructured text data. This includes building and maintaining an embeddings store, training classification models, and fine-tuning transformers.
• You’ll help scale our ML-powered features to cater for the ever-growing customer base of Up. Features such as intelligent customer chat routing and fraud detections.
• You will play a vital role in the entire lifecycle of our ML projects. You can expect to take on significant responsibility in designing, building, and delivering solutions to some of our most interesting problems.
• When we have a gnarly problem like how to serve a model in real-time with low latency, or how to detect and mitigate model drift, we’ll look to you to help design and implement a robust solution.

What tools we use

Experience in some or all of the tools that we use will be beneficial, but we can give you the space to learn anything you don’t know already:

• Google Cloud is our cloud platform of choice. Our data lives in BigQuery and Postgres, and our applications run on Kubernetes. All of that is managed with Terraform.
• Dagster orchestrates our data ELT, and we are expanding its use to manage the full MLOps lifecycle for our models — from training, evaluation, and experimentation, all the way to deployment.
• Most of our code is written in Python and SQL. Most of our custom models are written with PyTorch, though we use off-the-shelf models in Google Cloud to fill in the gaps when a custom build isn't needed.
• Regardless of language or framework, we’re looking for someone with strong software engineering fundamentals who can apply them to building robust and maintainable ML systems.

What skills you’ll bring

• Experience managing the full lifecycle of machine learning models: from data acquisition and feature engineering to training, validation, deployment, and monitoring in a production environment.
• An opinion on what makes a good ML system, particularly when it comes to reproducibility, testing, and maintainability. Good communication skills will be key.
• Experience in building and operating the infrastructure that supports ML systems. You're comfortable thinking about not just the model, but the entire system around it, from CI/CD pipelines to serving infrastructure. Experience with NLP, including embeddings and tuning transformers, is a big plus.
• An intrinsic motivation to collaborate and share knowledge with the engineers around you, helping us all raise the bar for how we build intelligent systems.
• An understanding of how ML can be used beyond just prediction; to automate processes across the organisation, to develop new personalised features for Up’s customers, and to glean insight into the future.

#LI-Hybrid

 

Working at Ferocia

We have a hybrid work culture where people can attend the Ferocia office as much or as little as makes sense for them, but we are currently only hiring in Melbourne (or adjacent areas) as we still value physically getting together at least a half-dozen times per year.

We offer:

• A small team of passionate people
• Generous leave and parental policy
• Flexible working schedule
• Great city office and perks (rooftop, gym and personal trainer, games…)
• Budget for personal development, training, and conferences
• Employee Assistance Program via Sonder
• Home loan rebates for our loans (conditions apply)
• Ongoing equity grants (conditions apply)

Not quite ticking every box? Throw your hat in the ring anyway! At Ferocia, we're all about shaking things up and rewriting the rules. We thrive on diversity and inclusion, and we wholeheartedly encourage you to step up and shine. Let us be the judge of your qualifications for this role – you just might surprise yourself!