Machine Learning Engineer
Summary
Build real-time computer vision models in Python/C++ to track movement in airports and smart spaces, using NVIDIA DeepStream and cloud infrastructure.
An Australian-owned tech company that's built a proprietary data platform processing billions of data points a day, pulled in from IoT sensors deployed across some of the world's busiest physical spaces. Think major airport terminals, stadiums, and smart city infrastructure. The platform turns raw sensor data into insight on how people move: wayfinding, queue management, passenger flow, operational efficiency. It's a small, technical team doing genuinely hard computer vision work at scale, with the culture to match: flexible, inclusive, and big on personal development (birthday leave and an annual L&D allowance included).
Role and responsibilities
You'll join the engineering team as a Machine Learning Engineer, working closely with the ML Lead on the company's computer vision roadmap for airport environments. Day to day that means building and refining models that track movement through terminals in real time, improving flow and adding a layer of automated spatial intelligence to how these spaces are monitored and managed.
You'll design and build production codebases and pipelines in Python and C++, apply ML techniques spanning computer vision, spatial analytics and time-series forecasting, and work closely with data engineers and product to ship high-throughput, low-latency solutions. You'll also help keep engineering standards high: strong code review culture, CI, and solid agile practice.
Key technical skills
- 4+ years as a Machine Learning Engineer or Data Scientist
- Strong Python and SQL, plus hands-on C++ for performance-critical work
- Real experience with computer vision SDKs, ideally NVIDIA DeepStream and GStreamer, for real-time video analytics
- Solid grounding in ML techniques, statistical analysis, time-series forecasting and deploying real-time inference models
- Comfortable with Docker and Kubernetes, cloud infrastructure (AWS preferred)
- Git, agile ways of working, and the ability to communicate clearly with technical and non-technical stakeholders alike
An Australian-owned tech company that's built a proprietary data platform processing billions of data points a day, pulled in from IoT sensors deployed across some of the world's busiest physical spaces. Think major airport terminals, stadiums, and smart city infrastructure. The platform turns raw sensor data into insight on how people move: wayfinding, queue management, passenger flow, operational efficiency. It's a small, technical team doing genuinely hard computer vision work at scale, with the culture to match: flexible, inclusive, and big on personal development (birthday leave and an annual L&D allowance included).
Role and responsibilities
You'll join the engineering team as a Machine Learning Engineer, working closely with the ML Lead on the company's computer vision roadmap for airport environments. Day to day that means building and refining models that track movement through terminals in real time, improving flow and adding a layer of automated spatial intelligence to how these spaces are monitored and managed.
You'll design and build production codebases and pipelines in Python and C++, apply ML techniques spanning computer vision, spatial analytics and time-series forecasting, and work closely with data engineers and product to ship high-throughput, low-latency solutions. You'll also help keep engineering standards high: strong code review culture, CI, and solid agile practice.
Key technical skills
- 4+ years as a Machine Learning Engineer or Data Scientist
- Strong Python and SQL, plus hands-on C++ for performance-critical work
- Real experience with computer vision SDKs, ideally NVIDIA DeepStream and GStreamer, for real-time video analytics
- Solid grounding in ML techniques, statistical analysis, time-series forecasting and deploying real-time inference models
- Comfortable with Docker and Kubernetes, cloud infrastructure (AWS preferred)
- Git, agile ways of working, and the ability to communicate clearly with technical and non-technical stakeholders alike
Surry Hills, Sydney. Hybrid, 3 days in office. $160k to $170k package.
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