Melbourne
6 months ago

Job Overview

Job Type
Contract
Pay
Not disclosed

Job description

Location:
Melbourne, Australia
Work arrangement:
On-site

Role Summary

About Mindbeam

We are building the next-generation AI infrastructure for open source and enterprise. Our work is deeply research-oriented and focused on developing groundbreaking innovations to take state-of-the-art AI applications to the next level.

Mission

Design and deliver AI solutions that enable enterprises to deploy, scale, and optimize generative AI workloads with speed, efficiency, and reliability.

Melbourne, Victoria, Australia

Role Expectations

  • Architect and optimize end-to-end AI infrastructure solutions using Mindbeam’s frameworks.
  • Develop integrations with channel partner technologies.
  • Guide clients through technical evaluations, deployments, and optimizations.
  • Collaborate with internal teams to align architecture with customer needs.
  • Present solutions clearly in client-facing engagements.

Background

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field—or equivalent work experience.
  • 2+ years of experience in AI/ML architecture, implementation, or technical consulting.
  • Strong knowledge of ML frameworks (PyTorch, TensorFlow, JAX) and distributed computing.
  • Hands-on expertise with Kubernetes (EKS/EC2), Docker, and cloud platforms (AWS, NVIDIA).
  • Familiarity with DevOps practices, security, and compliance in enterprise settings.
  • Excellent problem-solving skills in production environments.

About You

You combine technical depth with strong communication skills, making you a trusted partner to both engineers and executives. You approach challenges with curiosity, creativity, and a willingness to experiment boldly.

Role:
Solutions Architect - Contractor
Job Type:
Contract

Company profile

Mindbeam

mindbeam.ai

Mindbeam (Mindbeam AI) develops Litespark, a language model framework that uses advanced algorithms to speed up training and inference workloads for generative AI applications. Litespark is a zero-code PyTorch drop-in that reaches convergence faster on the same hardware, reducing training time, energy consumption and iteration cycles without changes to existing models or workflows. Its performance is benchmarked on B200 and H100 infrastructure.