Machine Learning Engineer - ML Ops

The Machine Learning (ML) Systems Engineer is a key architect of the platform that empowers our teams to build, deploy, and operate AI models at scale. You will design and build the core infrastructure, pipelines, and tools supporting everything from traditional ML to Large Language Models (LLMs). This is a high-impact software engineering role for those passionate about building robust, scalable systems that improve developer velocity and enable the effective application of AI across the organization.

Key Responsibilities

• Build the Core Platform: Design, build, and operate our ML infrastructure on Kubernetes for model training and inference.
• Develop Force-Multiplier Tools: Create tooling to streamline the end-to-end ML/LLM lifecycle (e.g., experiment tracking, RAG systems, model observability).
• Drive Best Practices: Design and implement MLOps and AIOps principles to improve automation, reliability, and security.
• Collaborate and Enable: Work closely with Data Scientists and ML Engineers as your internal customers to understand their needs and accelerate their work.

Personal Attributes we love to see:

• Pragmatism: While extensive knowledge of ML theory is highly valued, pragmatism wins over elaborate theory when it comes to shipping products that work.
• Collaboration: We believe data science is a team sport, and are after candidates who can communicate well, share knowledge, and be open to taking on ideas from anyone in the team. Having worked on shared code-bases in a commercial environment is a big plus, but it's the attitude that matters most.
• Technical Skills: A decent base of python and linux are key to a role in the team. Other than that, we're pretty flexible - we know tools are changing rapidly, and will continue to do so for many years to come. Experience with tools like Kubernetes, Helm, PyTorch, Terraform, Prometheus etc. are highly valued, but not mandatory.
• Attention to detail: Showing attention to detail when it counts is important.

Key Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• 2+ years of experience in software engineering, writing production-grade code.
• Strong proficiency in Python within a Linux/Unix environment.
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
• Solid grasp of modern software development practices (Git, CI/CD, automated testing).
• Highly Desirable
  • Experience building or managing infrastructure on a major cloud platform (AWS, GCP, Azure).
  • Familiarity with Infrastructure as Code (IaC) tools like Terraform or Pulumi.
  • Practical experience with the MLOps/LLMOps lifecycle and its ecosystem (e.g., vector DBs, serving frameworks).
  • Experience with observability stacks (Prometheus, Grafana) or managing large-scale GPU workloads.

Some of our benefits

Nearmap takes a holistic approach to our employees’ emotional, physical and financial wellness. Some of our current benefits include:

• Quarterly wellbeing day off - Four additional days off annually for your 'YOU' Days
• Access to LinkedIn Learning
• 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!

Working at Nearmap
We move fast and work smart; often wearing multiple hats. We adapted to remote working with ease and are continually looking at ways to improve. We’re proud of our inclusive, supportive culture, and maintain a safe environment where everyone feels a sense of belonging and can be themselves.

If you can see yourself working at Nearmap and feel you have the right level of experience, we invite you to get in touch. 

Read the product documentation for Nearmap AI:

https://docs.nearmap.com/display/ND/NEARMAP+AI

For a deep dive into Nearmap AI, listen to AI Systems Senior Director Mike Bewley on the Mapscaping podcast https://mapscaping.com/blogs/the-mapscaping-podcast/collecting-and-processing-aerial-imagery-at-scale

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