Machine Learning Engineer | Computer Vision & Deep Learning Up to A$180k + Super + Equity

About the CompanyComputer Vision | PyTorch | Deep Learning | Edge AI Build the models. Own the engineering. See them working in the real world. We’re working with a rapidly growing technology company building sophisticated AI and computer vision products used across complex real-world environments. They already have a successful global product, strong customer adoption and an established engineering team — and are now significantly expanding their machine learning capability as they grow internationally.About the RoleThey’re looking for a Machine Learning Engineer who can do much more than connect existing AI APIs together. You’ll be someone who has actually designed, trained, improved and deployed machine learning models into production. This is very much a hands-on ML engineering role.ResponsibilitiesWhat you'll be doingYou’ll work closely with product, software engineering and a small but highly capable ML team to build new machine-learning capabilities from the ground up.A major focus will be computer vision and deep learning, working with large volumes of real-world visual data and turning that data into reliable production ML systems.You’ll be involved across the complete ML lifecycle:Designing and developing custom computer vision and deep-learning modelsBuilding and improving models using Python and PyTorchDeveloping end-to-end model training pipelinesSourcing, cleaning, curating and labelling training dataRunning experiments and evaluating model performanceImproving accuracy, latency and inference performanceTaking models from experimentation through to productionBuilding software around the ML lifecycleModel versioning, deployment and monitoringWorking with both cloud and potentially edge inferenceExploring LLMs, multimodal models and emerging generative AI where they genuinely improve the productThis isn't a research role where models finish their lives inside a notebook. The expectation is that what you build ultimately becomes part of a commercial software product used by real customers.QualificationsWhat we're looking forIdeally you'll have around 4–8 years' experience across machine learning engineering, although exceptional candidates outside that range will absolutely be considered.The important part is the depth of your experience.Required SkillsYou should have strong experience with:Computer Vision You have genuinely worked with image-based ML problems and can discuss the models you've built, the problems they solved and how you improved their performance.Deep Learning Strong understanding of modern deep-learning approaches rather than only traditional machine-learning techniques.PyTorch You'll ideally have substantial hands-on experience building and training models using PyTorch or closely related frameworks.Custom Model Development This is particularly important. We're interested in people who have gone beyond simply consuming pretrained models or third-party AI services. You should have experience taking ownership of model development — training, experimentation, evaluation, optimisation and productionisation.Software Engineering You're an engineer first. You can write high-quality production software and are comfortable working alongside experienced software engineers. You understand that putting an ML model into production involves considerably more than achieving a good accuracy score.Production ML You understand areas such as training pipelines, model evaluation, versioning, deployment, monitoring, data pipelines, inference and performance optimisation.Particularly interesting backgroundsYou may have worked in areas such as:Computer VisionRoboticsAutonomous SystemsIndustrial AISmart CamerasMedical ImagingSatellite ImageryDefence TechnologyIoTPhysical AIImage RecognitionVideo AnalyticsEdge AIMultimodal AIExperience deploying machine-learning models to edge devices or resource-constrained environments would be particularly interesting.Preferred SkillsWhat this role is NOTThis isn't a traditional Data Scientist position.It's not primarily analytics, dashboards, forecasting or statistical modelling.It's not an MLOps-only role.It's not a pure academic research position.And it's not an "AI Engineer" position where most of your work involves calling OpenAI APIs, building RAG systems or connecting existing foundation models together.Generative AI is absolutely part of the company's thinking — and experience with LLMs or Vision Language Models is valuable — but the foundation of this role is serious machine learning engineering, computer vision and deep learning.You'll probably thrive here if...You've worked in a startup, scale-up or smaller engineering environment where people own problems rather than narrow pieces of them.You're comfortable with ambiguity.You like starting with first principles.You can move between experimentation and engineering.And when somebody asks: "Why is this model behaving this way?" …you genuinely want to find out.Pay range and compensation packageThe opportunityYou'll join a profitable and growing technology business whose product is already being used internationally.The company has raised institutional funding, has excellent customer retention and is now entering another significant phase of international expansion.The ML team is deliberately small, meaning you'll have genuine influence over the architecture, models, tooling and direction of the company's AI capability.There's also considerable scope over time to work across increasingly sophisticated computer vision, edge AI, multimodal and generative AI problems.