Senior ML Engineer - Geospatial Vision for Eco AI (Hybrid)

SLR Consulting Australia Pty Ltd; Western Australia; On-site; AUD 150,000 - 190,000; Full time

Benefits offered by this job

Competitive salary

Flexible hybrid work

Job summary

Tyton Ecological Intelligence is seeking a Senior Machine Learning Engineer to evolve TytonAI, our large-scale computer vision platform for ecological classification.

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

Qualifications

• 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.

Responsibilities

• 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

Machine Learning

Computer Vision

Python

PyTorch

MLOps

GCP

Geospatial imagery

Tools

Python

PyTorch

Job description

Tyton Ecological Intelligence is seeking a Senior Machine Learning Engineer to evolve TytonAI, our large-scale computer vision platform for ecological classification.

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