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Senior Machine Learning Engineer - Job Opportunity at Nearmap

Barangaroo, Australia
Full-time
Senior
Posted: July 3, 2025
Hybrid
AUD 140,000 - 180,000 per year based on senior-level ML engineering roles in Sydney's competitive tech market, with potential for equity participation given Nearmap's growth trajectory and global expansion

Benefits

Quarterly wellbeing day off providing four additional personal days annually for mental health and work-life balance
Access to LinkedIn Learning platform for continuous professional development and skill enhancement
Wellbeing and technology allowance providing financial support for health and productivity tools
Annual flu vaccinations demonstrating company commitment to employee health and preventive care
Hybrid work flexibility allowing optimal work-life integration and reduced commuting stress
Complimentary Nearmap subscription providing direct access to company's premium location intelligence platform
Fully stocked kitchen with unlimited snacks and refreshments throughout the workday
Catered in-office lunch service every Tuesday and Thursday at Sydney CBD office fostering team collaboration
On-site shower facilities supporting active lifestyle and fitness routines for cyclists and gym users
Free food and meal provisions

Key Responsibilities

Architect and optimize mission-critical backend services, APIs, and databases that form the foundation of Nearmap's AI-powered location intelligence platform, directly impacting the quality and scale of machine learning operations
Design and maintain sophisticated labeling and data management systems using cutting-edge technologies including Python, SQL, Kubernetes, Argo, and GitLab, ensuring robust infrastructure for large-scale AI model training
Collaborate strategically with data scientists and ML engineers to create seamless, efficient workflows across diverse machine learning applications, bridging the gap between research and production systems
Lead architectural decision-making processes and ensure system designs align with organizational goals and industry best practices, influencing the technical direction of Nearmap's AI capabilities
Mentor engineering talent across all experience levels while championing collaborative excellence and operational reliability, contributing to team growth and technical culture development

Requirements

Education

Bachelor's or Master's degree in Computer Science, Engineering, or related technical field

Experience

5+ years building scalable, production-grade software systems (ideally in ML/MLOps environments)

Required Skills

Strong Python skills Deep experience with distributed systems on Linux Proven track record with CI/CD pipelines, monitoring, and production optimisation Excellent collaboration skills Docker Kubernetes Snowflake AWS services (S3, RDS, EKS)
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Sauge AI Market Intelligence

Industry Trends

The geospatial intelligence and location analytics market is experiencing unprecedented growth driven by increased demand for real-time imagery and AI-powered insights across industries including urban planning, construction, insurance, and logistics. Companies are increasingly investing in proprietary data collection and processing capabilities to differentiate their offerings and maintain competitive advantages in this rapidly evolving sector. Machine learning operations (MLOps) and data engineering roles are becoming increasingly critical as organizations recognize that model performance is fundamentally dependent on data quality and pipeline reliability. The industry is shifting focus from pure model development to building robust, scalable infrastructure that can handle massive datasets and ensure consistent model performance in production environments. The integration of computer vision, geospatial analysis, and cloud computing technologies is creating new opportunities for specialized engineering roles that combine traditional software engineering with domain-specific knowledge of imagery processing, spatial data management, and distributed computing architectures. Australian technology companies are increasingly competing on the global stage, particularly in specialized niches like location intelligence, creating demand for senior engineering talent who can build world-class products while navigating the unique challenges of scaling from the Australian market to international expansion.

Role Significance

Likely part of a 8-12 person AI Data Engine team within a larger AI/ML organization, collaborating closely with data scientists, ML engineers, and platform engineers across multiple product initiatives
Senior individual contributor role with significant architectural influence and mentoring responsibilities, positioned as a technical leader within the AI Data Engine team who will shape the foundational systems that power Nearmap's entire AI product suite

Key Projects

Building next-generation data pipeline architectures capable of processing petabytes of high-resolution imagery data Implementing intelligent labeling systems that optimize human annotation workflows and improve model training efficiency Developing robust MLOps infrastructure that supports rapid experimentation while maintaining production reliability Creating self-improving systems that automatically optimize resource allocation and model performance based on real-world feedback

Success Factors

Deep understanding of distributed systems architecture and the ability to design solutions that scale from prototype to production while handling massive geospatial datasets Strong collaboration skills and the ability to translate between technical and business requirements, particularly when working with data scientists and product teams to implement complex AI workflows Expertise in production systems monitoring, debugging, and optimization, with a focus on reliability and performance in mission-critical applications Leadership capabilities and mentoring skills to guide junior engineers while contributing to technical culture and best practices across the organization

Market Demand

High demand driven by the convergence of AI, geospatial technology, and cloud infrastructure, with limited supply of engineers who have both ML systems experience and the ability to work at scale in production environments

Important Skills

Critical Skills

Python programming expertise is fundamental given its dominance in ML infrastructure and data processing, with deep knowledge required for building scalable services and APIs that handle complex geospatial data workflows Distributed systems experience is essential for handling the massive scale of imagery data and ensuring reliable performance across global operations, particularly important for real-time processing and analytics CI/CD and production systems knowledge is crucial for maintaining reliable ML pipelines and ensuring smooth deployment of AI models in production environments where downtime directly impacts customer operations

Beneficial Skills

Kubernetes and containerization expertise provides significant value in modern ML infrastructure, enabling efficient resource management and scalable deployment of AI services across cloud environments AWS cloud services knowledge is valuable for leveraging managed services and ensuring cost-effective, scalable infrastructure that can handle variable workloads and global data distribution Experience with data warehousing solutions like Snowflake becomes increasingly important as organizations focus on creating unified data platforms that support both analytics and ML workflows

Unique Aspects

Opportunity to work on cutting-edge computer vision and geospatial AI technologies that have direct real-world impact across multiple industries
Focus on building self-improving AI systems that optimize both model performance and operational efficiency, representing the next generation of MLOps architecture
Integration of proprietary hardware (camera systems) with advanced software platforms, providing exposure to full-stack technology development
Role emphasis on backend systems and infrastructure rather than model development, making it ideal for engineers who want to impact AI without deep ML research focus

Career Growth

Typical progression to principal level within 2-3 years, with management track opportunities emerging after 3-5 years depending on organizational growth and individual preferences

Potential Next Roles

Principal Machine Learning Engineer with broader architectural responsibilities across multiple product lines ML Engineering Manager leading teams of engineers building AI infrastructure Staff Engineer or Technical Lead for AI/ML Platform Engineering VP of Engineering or CTO roles at AI-focused startups or geospatial technology companies

Company Overview

Nearmap

Nearmap is a well-established Australian technology company that has successfully carved out a unique position in the global location intelligence market through proprietary aerial imagery capture technology and AI-powered analytics. The company has demonstrated strong growth and international expansion, with operations across Australia, New Zealand, and North America, serving diverse industries including construction, insurance, government, and utilities.

Strong market position as a specialized provider of high-resolution aerial imagery and location analytics, competing against larger players like Google Earth and specialized geospatial companies through technological innovation and market focus
Significant presence in the Australian tech ecosystem with headquarters in Sydney, representing an opportunity to work for a locally-founded company with global reach and ambitions
Fast-paced, collaborative environment that values innovation and technical excellence, with strong emphasis on work-life balance and employee wellbeing as evidenced by comprehensive benefits package and flexible work arrangements
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