Senior ML Engineer - Computer Vision & Production Systems

Mantel; City of Melbourne; On-site; AUD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive compensation and equity

Health insurance

5% kiwisaver contribution

EAP & wellbeing support

Job summary

Timescapes is seeking a Machine Learning Engineer to join our growing team. You will work with the product and engineering teams to develop ML models (computer vision) and integrate them into our products, tracking activity and progress on construction sites.

You will run experiments with LLMs and other generative AI models, decide between custom development vs off-the-shelf models, and build software to manage the ML lifecycle including data management, labeling, and training.

Qualifications

• Bachelor’s degree in CS/Engineering or related field.
• Production ML software systems with core ML components experience.
• CV models development from data and model sides.
• End-to-end ML operations: data, labeling, training, CI, versioning, monitoring.
• Experience with LLMs, LVMs, and multimodal models.

Responsibilities

• Design, develop, integrate, and support ML models for construction problems.
• Run experiments with LLMs and other large-scale generative AI models.
• Develop software to manage ML lifecycle: data management, labeling, training.
• Source training data and integrate it into the ML lifecycle.

Skills

Production ML

Computer vision

ML lifecycle

LLMs/LVMs multimodal

ML validation

ML inference deployment

SDLC integration

Education

Bachelor’s degree in CS/Engineering

Tools

PyTorch

TensorRT

.NET

C#

Python

Job description

Timescapes is seeking a Machine Learning Engineer to join our growing team. You will work with the product and engineering teams to develop ML models (computer vision) and integrate them into our products, tracking activity and progress on construction sites.

You will run experiments with LLMs and other generative AI models, decide between custom development vs off-the-shelf models, and build software to manage the ML lifecycle including data management, labeling, and training.