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Mindbeam

GPU-accelerated AI training and inference framework

Solutions Architect

Implementation & Technical Account ManagementConfirmed live in the last 24 hours
ContractMelbourne VIC, AustraliaNo salary listedHybrid

About the Job

Requirements

  • A Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent work experience.
  • At least 2 years of experience in artificial intelligence/machine learning architecture, implementation, or technical consulting.
  • Strong knowledge of machine learning frameworks including PyTorch, TensorFlow, and JAX, along with distributed computing.
  • Hands-on expertise with Kubernetes, including Amazon Elastic Kubernetes Service and Amazon Elastic Compute Cloud, Docker, and cloud platforms including Amazon Web Services and NVIDIA.
  • Familiarity with DevOps practices, security, and compliance in enterprise settings.
  • Excellent problem-solving skills in production environments.

Responsibilities

  • Architect and optimize end-to-end artificial intelligence 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.

About the Company

Mindbeam

GPU-accelerated AI training and inference framework

Mindbeam builds AI infrastructure tools to speed up large language model work. Its main product, Litespark, accelerates pre-training and inference for generative AI by optimizing GPU usage, and it runs on existing hardware without requiring changes to users’ code, while staying compatible with PyTorch, TensorFlow, and JAX. The company offers Litespark via the AWS Marketplace for SageMaker and provides Mindbeam Pro, a professional-services suite where optimization experts help teams implement acceleration, fine-tuning, and strategic roadmaps. Its goal is to make large-model training and deployment more affordable and practical for both enterprises and open-source projects, enabling scalable generative AI.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Not listed
Founded
2024

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