Faculty is one of the most ambitious AI companies operating today. Founded in 2014, the company has worked with over 350 global customers, delivering responsible AI solutions across government, finance, retail, energy, life sciences, and defence. The TEST Australia role based in Sydney represents an opportunity to join a fast-growing team at the frontier of applied artificial intelligence.
But what does it actually take to get hired here? Understanding the skills Faculty values helps you assess your readiness and plan your next move.
Who Faculty Is Looking For
Faculty does not chase hype. The company builds and deploys AI that creates measurable, real-world impact. That means the people they hire must combine deep technical ability with sound judgment and genuine intellectual curiosity.
Faculty works across complex industries. You will interact with senior stakeholders in regulated sectors. That environment demands both precision and strong communication.
The company also emphasises diversity of thought. Faculty actively encourages applications from people of all backgrounds, genders, ethnicities, religions, and sexual orientations. The focus is on finding people who share a commitment to using technology for positive, lasting impact.
Technical Skills Required for the TEST Australia Role
Working at Faculty requires a solid technical foundation. AI and machine learning are central to everything the company does. The following technical skills are consistently valued across Faculty's project teams.
Core AI and Machine Learning Knowledge
You need a working understanding of machine learning fundamentals. This includes supervised and unsupervised learning, model evaluation, and feature engineering. Familiarity with large language models and modern AI architectures is increasingly important.
Faculty deploys AI in real production environments, not just research settings. Knowing how to move models from development into live systems is a critical differentiator. Experience with MLOps practices adds significant weight to an application.
Programming and Data Skills
- Python proficiency is essential for most technical roles at Faculty
- Experience with data manipulation libraries such as Pandas and NumPy
- Familiarity with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn
- SQL knowledge for working with structured datasets
- Understanding of cloud platforms such as AWS, GCP, or Azure
- Version control using Git and collaborative development workflows
Software Engineering Practices
Faculty builds products that clients depend on. Clean, maintainable code matters. You should understand software engineering best practices, including testing, documentation, and code review processes.
Familiarity with containerisation tools like Docker or Kubernetes is valuable. Many of Faculty's deployments run in cloud-native environments. Knowing how infrastructure fits together gives you a practical edge.
Soft Skills That Faculty Values
Technical skills alone will not get you far at Faculty. The company hires people who can think critically, communicate clearly, and work effectively within high-performing teams.
Intellectual Curiosity
Intellectual curiosity is perhaps the most repeated phrase in Faculty's hiring language. They want people who ask hard questions and challenge assumptions. Candidates who treat learning as a lifelong habit stand out clearly.
This is not about knowing everything. It is about the drive to understand deeply. Faculty values people who go beyond surface-level understanding.
Communication and Stakeholder Management
Faculty works with clients across government and defence sectors. These are high-stakes environments where clarity matters. You need to translate complex technical findings into plain language that non-technical stakeholders can act on.
- Written communication that is concise and precise
- Verbal communication confident enough for senior client meetings
- The ability to listen actively and interpret business problems correctly
- Presentation skills that make data and AI outputs accessible
Problem-Solving and Critical Thinking
AI projects rarely go exactly as planned. Clients bring messy, ambiguous problems. Structured problem-solving helps you break those problems into solvable parts and prioritise what matters most.
Critical thinking also means knowing when a machine learning solution is not the right answer. Faculty's reputation depends on honest, well-reasoned advice. Intellectual honesty is not optional.
Collaboration and Teamwork
Faculty operates with hybrid working across a diverse team. Remote collaboration tools and asynchronous workflows are part of daily life. You should be comfortable contributing across time zones and working independently without losing team alignment.
Respecting different perspectives is essential. Faculty explicitly builds teams with diverse backgrounds because they believe that produces better results. A collaborative mindset is not a nice-to-have. It is a baseline expectation.
Experience That Strengthens Your Application
Faculty does not publish rigid experience requirements for every role. However, certain backgrounds consistently align well with what the company builds and how it operates.
Industry Experience
Prior exposure to sectors like government, finance, energy, life sciences, or defence is genuinely useful. Understanding the constraints and priorities of regulated industries helps you ask better questions and deliver more relevant solutions.
That said, Faculty encourages applications even when you do not meet every listed requirement. If you bring strong foundational skills and genuine curiosity, the company is open to the conversation.
Project Delivery Experience
Experience delivering AI or data science projects end-to-end is highly relevant. This means taking a problem from initial scoping through data exploration, model development, testing, and deployment. Real project experience demonstrates that you can handle the full lifecycle, not just isolated parts of it.
- Agile or iterative project delivery experience
- Experience presenting findings to clients or senior stakeholders
- Work on cross-functional teams with engineers, analysts, and product managers
- Exposure to responsible AI principles, ethics, or governance frameworks
How to Build the Skills Faculty Is Looking For
If you are early in your career or transitioning into AI, there are practical steps you can take to strengthen your profile. Building these skills takes time, but the path is well-established.
Deepen Your Technical Foundation
Start with Python and progress to core machine learning libraries. Platforms like Coursera, fast.ai, and DeepLearning.AI offer structured learning paths. Work through projects that use real datasets, not toy examples.
Contribute to open-source projects on GitHub. This builds practical skills and creates a visible record of your work. Employers like Faculty can see how you write code and approach problems when your work is public.
Practice Communicating About Technical Work
Write about what you build. Start a blog, publish on LinkedIn, or create short case study write-ups. Explaining your work clearly to a non-technical audience is a skill that requires deliberate practice.
Join local data science or AI meetups in Sydney. Presenting your projects, even informally, builds confidence and sharpens your thinking. The Sydney tech community is active, and connections made there often lead to real opportunities.
Seek Out Real Project Experience
Competitions on Kaggle provide structured problem-solving experience. Volunteer data projects with nonprofits offer exposure to real business constraints. Internships or contract roles at smaller AI companies can accelerate your practical development significantly.
Focus on projects that mirror the sectors Faculty serves. A project analysing public health data or modelling energy consumption shows sector awareness alongside technical skill. That combination is harder to fake and easier to discuss in an interview.
Build a Portfolio That Tells a Story
A strong portfolio shows not just what you built, but why decisions were made. Document your thinking, the tradeoffs you considered, and what you learned from failure. Faculty values truth-seeking, and a portfolio that reflects honest reflection will resonate with their hiring team.
When you are ready to apply, you can submit your application directly through the Faculty listing on Remote OK. Visit the posting at https://remoteOK.com/remote-jobs/remote-test-australia-faculty-1133486 to learn more and take the next step toward joining one of Australia's most forward-thinking AI teams.
