Senior Machine Learning Engineer | Applied Deep Learning for Real-World Systems

Location: Sydney, Australia (hybrid working)

Type: Full-time | Defence & AI Technology

About the Opportunity

We’re partnered with a high-growth technology company operating at the intersection of AI, electronic warfare, and multi-sensor systems. Their mission: to build advanced sensing and decision platforms that detect, classify, and respond to emerging threats in real time helping safeguard people and infrastructure worldwide.

After significant capital investment and rapid global expansion, the company is growing its AI/ML engineering division to push the limits of applied deep learning in defence and security contexts.

The Role

As a Senior ML Engineer you’ll design, train, and deploy deep learning models for RF, acoustic, and sensor-fusion domains, translating state-of-the-art research into production systems that operate in the field, not the lab.

You’ll own the full ML lifecycle:

• Research and prototype new model architectures for high-dimensional, real-world data.
• Optimise inference for embedded and edge devices (GPU, NPU, FPGA).
• Deploy and monitor models in live environments with hardware and systems teams.
• Push model efficiency, robustness, and performance under operational constraints.
• Contribute to ML infrastructure, best practices, and reproducibility standards.

This role blends the autonomy of research with the rigour of engineering. You’ll work alongside a world-class team spanning deep learning, embedded systems, RF, and sensor fusion solving problems that have genuine national-level impact.

About You

You’ll thrive here if you:

• Enjoy solving complex, ambiguous problems with real-world stakes.
• Have a solid grounding in ML/DL fundamentals and strong engineering discipline.
• Take pride in writing clean, reliable, production-ready code.
• Are motivated by building technology that protects, enables, and endures.

Your background likely includes:

• 3–8+ years in applied ML, DL, or AI research engineering.
• Strong experience with PyTorch, Python, and modern ML frameworks.
• Exposure to edge or embedded ML (e.g., Jetson, FPGA, TensorRT, ONNX).
• Familiarity with RF, acoustic, or multi-modal datasets a major plus.
• Proficiency with software engineering best practices (Git, testing, CI/CD).
• Bonus: Publications, open-source contributions, or hardware-aware AI optimisation.

Why Join

This is one of Australia’s fastest-growing deep-tech companies, blending mission, innovation, and impact. You’ll join a team where AI research becomes real-world capability, working on systems that are deployed globally across defence, security, and critical infrastructure.