ML Engineer - Computer Vision for Construction (Equity)

Timescapes; City of Melbourne; On-site; AUD 120,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Equity package

KiwiSaver 5%

EAP & wellbeing support

Job summary

Timescapes in Melbourne, VIC is seeking a Machine Learning Engineer to join a growing team. You’ll build computer vision models and integrate them into our product to track construction progress.

You’ll run experiments with LLMs, manage data and ML lifecycle tooling from labeling to deployment, and collaborate with product and engineering to deliver reliable, scalable features for customers worldwide.

Qualifications

• Bachelor's degree in CS, Engineering, or related field.
• Experience delivering production ML systems with ML components.
• Experience developing computer vision models (data and model).
• Experience end-to-end ML operations: data, labeling, training, CI, versioning, monitoring.
• Experience leveraging LLMs, LVMs and multimodal models to augment product features.
• Familiarity with ML model validation and verification.
• Experience deploying ML inference infrastructures for high throughput.
• Ability to contribute across SDLC from concept to maintenance.

Responsibilities

• Design, develop, integrate and support ML models for construction problems.
• Run experiments with LLMs and generative AI to choose build vs off-the-shelf.
• Develop software to manage ML lifecycle including data management, labeling, training.
• Source and curate training data and integrate it into lifecycle.

Skills

Bachelor's degree

Computer vision

MLOps

LLMs/LVMs

Multimodal models

Python

PyTorch

CI/CD

Education

Bachelor's degree in CS/Engineering

Tools

PyTorch

TensorRT

C#

Python

Edge ML

Job description

Timescapes in Melbourne, VIC is seeking a Machine Learning Engineer to join a growing team. You’ll build computer vision models and integrate them into our product to track construction progress.

You’ll run experiments with LLMs, manage data and ML lifecycle tooling from labeling to deployment, and collaborate with product and engineering to deliver reliable, scalable features for customers worldwide.