Machine Learning Engineer - Agentic AI

Nearmap; City of Melbourne; Hybrid; AUD 80,000 - 110,000; Full time

Benefits offered by this job

Quarterly wellbeing day off

Access to LinkedIn Learning

Technology allowance

Stocked kitchen

Job summary

A tech pioneer in location intelligence is seeking a Machine Learning Engineer to develop LLM-based solutions using Earth Observation data. You will work closely with data scientists to integrate models into workflows solving complex problems. The ideal candidate has a solid foundation in software development, machine learning fundamentals, and collaboration skills. This role offers a hybrid work model and several benefits including additional wellbeing days off.

Qualifications

• Formal education in a technical, data related field.
• At least 2 years experience writing production grade commercial software.
• Experience with LLM-powered systems is highly desirable.

Responsibilities

• Performs software engineering tasks for end-to-end AI systems.
• Designs, builds, and maintains components of an AI system.
• Collaborates with data scientists and other engineering teams.

Skills

Software development

Collaboration

Attention to detail

Machine learning fundamentals

Programming in Python

Linux environment

Education

Bachelor’s degree in computer science, engineering, statistics, or physics

Tools

Docker

Kubernetes

AWS

GCP

Git

Job description

Nearmap is the Australian‑founded, global tech pioneer innovating the location intelligence game. Customers rely on Nearmap for consistent, reliable, high‑resolution imagery, insights, and answers to create meaningful change in the world and propel industries forward. Harnessing its own patented camera systems, imagery capture, AI, geospatial tools, and advanced SaaS platforms, Nearmap stands as the definitive source of truth that shapes the livable world.

Machine Learning Engineer – Deep Learning Specialist

The Machine Learning (ML) Engineer will undertake technical work to design, prototype and develop LLM‑based products that utilise Earth Observation data, primarily Nearmap imagery and AI data. Where a data scientist typically focuses on the meaning in the data and producing accurate models, the ML Engineer focuses on integrating models and data into complex workflows that autonomously solve problems. The role requires solving a variety of challenging problems in software engineering, agentic AI design and evaluation, and close collaboration with data scientists as a peer.

Key Responsibilities

• Performs software engineering tasks required by an end‑to‑end AI system.
• Designs, builds and maintains subcomponents of an AI system, in collaboration with data scientists: microservices, APIs, working with technologies such as Docker, Kubernetes, MCP.
• Collaborates with other engineering teams and DevOps to ensure consistent best practices and integration of systems.
• Reviews code and work of peers.

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.

Qualifications

• Key Requirements: Formal education in a technical, data related field (Bachelor’s degree in computer science, engineering, statistics, physics, etc.), with an emphasis on software development.
• Ideally at least 2 years experience writing production grade commercial software in a team environment.
• Machine learning knowledge and experience with LLM‑powered systems is highly desirable, but a passion to learn more is sufficient. We see ML engineering as a sub‑field of software engineering, that benefits greatly from a good foundation in at least 10‑200 k words of practicing prompts, model fine‑tuning, etc.
• Mandatory: Programming/Tech Environments: Ability to code in scientific python, using a linux environment, and git for source control. Machine Learning: Appreciation of machine learning fundamentals. Engineering Approach: Follow best practices in modern software engineering, applying them to build robust, scalable machine learning systems.
• Highly Desirable: Domain Knowledge – NLP/LLM: Working on Machine Learning problems applied to text data. Software Engineering: Working on shared codebases to produce production quality code. Cloud Computing: Working on AWS or GCP using distributed virtual machines, docker containers, etc. GPU: Using GPUs to accelerate scientific computing. Deep Learning: Applying modern artificial neural networks to solve machine learning problems.
• Scale: Working with large data sets, where data sets don’t fit into memory, and require multiple nodes to compute efficiently.

Benefits

• 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.

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

Thanks, but we got this! Nearmap does not accept unsolicited resumes from recruitment agencies and search firms. Please do not email or send unsolicited resumes to any Nearmap employee, location or address. Nearmap is not responsible for any fees related to unsolicited resumes.