• Design, develop, and architect high-performance AI/ML services and pipelines in a cloud-based environment. • Contribute to cutting-edge R&D initiatives, including service integration with emerging frameworks and platforms. • Assist with the technical resolution of issues on mission-critical production systems, embracing the philosophy: "you build it, you own it, you run it." • Think outside the box to devise solutions that scale, are fault-tolerant, easy to grasp, and elegant.

• Develop large-scale distributed machine learning systems that are scalable, performant, efficient, and reliable • Collaborating with cross-functional teams to help deploy/integrate machine learning models. • Liaise with the BUs for their ML needs and work on the cross-BU ML portfolio. • Optimize feature extraction, transformation and selection. • Work with and manage Feature Stores for reusability across ML pipelines. • Ensure scalability, reliability, cost efficiency, and ease of use of the machine learning platform • Contribute to evaluating and adopting new technologies and tools to enhance our machine-learning capabilities

Role Description We are looking for a ML Engineer to work closely with the ML Architect to develop on ML frameworks (TensorFlow, Scikit-Learn, Pytorch), Experimentation platform and tools. This role has the responsibilities to: - Develop large-scale distributed machine learning systems that are scalable, performant, efficient, and reliable. - Collaborate with cross-functional teams to help deploy/integrate machine learning models. - Liaise with the BUs for their ML needs and work on the cross-BU ML portfolio. - Optimize feature extraction, transformation and selection. - Work with and manage Feature Stores for reusability across ML pipelines. - Ensure scalability, reliability, cost efficiency, and ease of use of the machine learning platform. - Contribute to evaluating and adopting new technologies and tools to enhance our machine-learning capabilities. Qualifications - 5+ years of experience as a Machine Learning Engineer. - Strong experience with supervised and unsupervised learning, survival analysis, time series modeling, and statistical forecasting. - Skilled in building predictive models (e.g. churn, user journey, sales forecasting) using behavioral data. - Proficient with ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn. - Experienced in model training, versioning, and monitoring. - Solid background in MLOps practices: CI/CD, Docker, Kubernetes, Airflow, SageMaker, MLflow, model observability tools, and feature stores. - Business-oriented mindset: ability to connect model outcomes to product and strategic goals. Benefits - Work with a bunch of great people in a politics-free environment. - Culture reflects a lean and self-organization attitude. - Encouragement to take risks, make decisions, and work collaboratively. - Value commitment, feedback, and empathy. Company Description At Mindera we use technology to build products we are proud of, with people we love. Software Engineering Applications, including Web and Mobile, are at the core of what we do at Mindera. We partner with our clients to understand their products and deliver high-performance, resilient, and scalable software systems that create an impact on their users and businesses across the world. - Our offices are located in: Porto, Portugal | Aveiro, Portugal | Coimbra, Portugal | Leicester, UK | San Diego, USA | San Francisco, USA | Chennai, India | Bengaluru, India | Cluj-Napoca, Romania | Blumenau, Brazil.

Role Description The maps that planners, insurers, and governments rely on to understand the physical world don't update themselves — someone has to build the systems that turn raw aerial imagery into ground truth. That's this team. This role is all about translating R&D from other parts of the Nearmap AI & Computer Vision team into data and ultimately, answers. You will be a core contributor to the algorithmic systems for creating conflated Map Data products. - Conflation is the process of combining multiple data observations about the world into a single, cohesive map that prioritises usability, practicality, and a straightforward representation of real world objects. - Input data sources include aerial imagery (multiple surveys over time, 2D, 3D, multi-angle, and captured by multiple providers), other geospatial sources such as property data, permit data or suitably licensed open data sets. - A key challenge is to provide transparency of data provenance and accuracy, while abstracting away as much complexity as possible. We design, build, and operate software systems that take petabytes of data to transform aerial imagery to insight. Our technology stack is based on the python scientific libraries and traverses machine vision deep learning technology such as Pytorch, and GIS tools such as GEOS, Shapely and GeoPandas/GeoPolars. We work mostly in python for speed of development and occasionally drop down to compiled libraries when we need to care about performance. A typical day: - You might spend a morning profiling a conflation pipeline that's producing unexpected artefacts near property boundaries. - Pair with a CV researcher in the afternoon to understand what their model's confidence scores actually mean for downstream data quality. - Finish the day reviewing a colleague's PR on a new spatial indexing approach. - Agentic coding tools like Claude Code are a normal part of how we work — not a novelty, but a practical accelerant for the kind of exploratory, iterative work this role involves. This is a fully remote role, open to candidates located on the East Coast of Australia (Melbourne, Sydney, Brisbane). The team operates with daily stand-ups, close collaboration, and a culture of open communication — expect regular pair programming, drop-in calls, and genuine team involvement in your work. Qualifications - Demonstrated 5+ year history working in a numerical field: e.g. applied maths, physical sciences, computer vision, geospatial analysis. - A demonstrated ability to produce high quality production numerical code with a focus on business outcomes. - Programming & tech environments: ability to code in scientific python, using a Linux environment, and git for source control. - Machine learning fundamentals: appreciation of core ML concepts including regularisation, hyperparameter optimisation, and validation methods. - Scientific approach: follow the scientific method of formulating hypotheses, and applying statistical tests to validate them. - Engineering approach: follow best practices in modern software engineering, applying them to build robust, scalable machine learning systems. - Strong approach to systems thinking, whilst remaining pragmatic. - Commitment to software engineering principles for scientific python, a keen eye for clean code, and a passion for robustness and correctness. Requirements - Working with large data sets, where data doesn't fit into memory, and requires multiple nodes to compute efficiently. - A scientific mindset of formulating hypotheses, and applying statistical tests to validate them. - Working in a cloud-native environment using highly scalable compute. - Experience with operationalizing numerical applications and workflows. - Scale: working on machine learning problems applied to image and geospatial data. Benefits - Quarterly wellbeing day off - Four additional days off annually for your 'YOU' Days. - Wellbeing and technology allowance. - Annual flu vaccinations. - Hybrid flexibility for this role. - Nearmap subscription (of course!). - Stocked kitchen with access to all the snacks you need. - In-office lunch every Tuesday and Thursday at our Sydney CBD office. - Showers available for anyone cycling to work or lunchtime gym-goers!