Machine Learning Engineer

Tyton Ecological Intelligence (Tyton) builds AI-first platforms for ecological monitoring at landscape scale.

Our products, TytonAI and TytonIQ, combine machine learning, high-resolution aerial imagery (drone, aircraft and satellite), and cloud analytics to automate vegetation classification, rehabilitation monitoring, weed mapping and biodiversity assessment across thousands of hectares.

Tyton is creating a globally scalable enterprise platform that enables ecologists and environmental teams to replace manual workflows with repeatable, ML-driven processes. TytonAI is already deployed across major mining operations in Western Australia, and we’re now expanding this capability for broader global use.

Our recent acquisition by SLR Consulting provides us with a global platform, driving us into an exciting period of growth. Learn more about Tyton and our technology at http://www.tytonei.com

We’re looking for a Senior Machine Learning Engineer to help evolve TytonAI, our large-scale computer vision platform for ecological classification. This is a permanent full-time role initially based in our Leederville office, with a transition to be based out of SLR Consulting’s Subiaco office, next year.

This is a hands-on senior role combining R&D and delivery. You’ll own core ML systems end-to-end: experimenting with new ideas, turning successful models into production pipelines, and continuously improving performance, reliability, and scale.

You’ll work on deep learning for high-resolution imagery, geospatial pipelines, and cloud workflows supporting enterprise customers globally. This role suits someone who enjoys shipping real ML products - not just building prototypes.

Key Responsibilties

Improve TytonAI’s core computer vision capabilities (segmentation, classification, fine-tuning)

Ideate, design, and run experiments focused on customer impact

Design, train, and deploy deep learning models for large-scale remote sensing and geospatial classification

Explore emerging ML techniques

Translate research ideas into production systems

Identify performance bottlenecks and improve inference speed, memory usage, and cloud efficiency

Contribute to platform architecture and technical direction as the product scales

Build tooling for training data, evaluation, and batch inference

Work within cloud environments (primarily Google Cloud) and Linux environments

Maintain documentation and engineering standards

Skills and Experience

3+ years experience in Machine Learning with Computer Vision.

Strong PyTorch and Python engineering skills.

Solid understanding of training, evaluation, deployment, and optimisation

Experience maintaining or extending existing ML codebases

Strong problem-solving ability with real-world data

Note that applicants must have valid working rights in Australia and be based in Perth or be willing to relocate.

Desirable

Geospatial / remote sensing experience.

Exposure to Ecology

Experience working with large GIS imagery datasets (satellite, drone, aerial)

MLOps experience (CI/CD for ML, model monitoring, reproducibility)

Cloud computing experience, GCP preferred.

Our Benefits

A technical role with meaningful ownership of core ML systems

Fast innovation cycle from idea → experiment → production

Real-world ML problems at meaningful scale

Competitive salary + flexible hybrid work

Small, high-calibre team with real product influence

Perth-based, practical, focused, low-ego culture

Next Steps

The problems here are genuinely interesting. You'll be working on proprietary ML infrastructure applied to real ecological data, in a domain where the technical challenges and the environmental stakes are both real. The team is small and experienced, which means less overhead and more ownership, and our recent acquisition by SLR Consulting gives us a global platform from which to scale.

Apply with your CV if you’d like to grow alongside a business doing something that matters.