Mindbeam AI

Machine Learning Engineer

• Design and implement custom GPU/accelerator kernels to maximize performance.
• Profile, benchmark, and optimize critical ML workloads.
• Collaborate with researchers to translate algorithmic advances into efficient, production-ready code.
• Stay current with hardware advancements (CUDA, ROCm, TPU) to inform kernel design.
• Document and share best practices for low-level optimization.

Machine Learning Engineer – Kernels

• Develop pipelines for post-training tasks such as fine-tuning, evaluation, and model compression.
• Implement scalable systems for model deployment, monitoring, and optimization.
• Collaborate with researchers to validate experimental results in production contexts.
• Build tools to automate benchmarking and regression testing.
• Identify opportunities to improve efficiency in resource utilization and inference speed.

Machine Learning Engineer – Post Training

Solutions Engineer

• 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.

Machine Learning Engineer – Kernels

Machine Learning Engineer – Post Training

Solutions Architect – Contractor