Mindbeam AI

Machine Learning Engineer

• Build scalable pre-training pipelines for foundation models, optimizing throughput and efficiency.
• Implement distributed training strategies across GPUs/TPUs and high-performance clusters.
• Collaborate with researchers to translate experimental setups into production-ready workflows.
• Develop monitoring and fault-tolerance systems to ensure reliable large-scale training.
• Continuously benchmark and tune performance across hardware and software stacks.

Machine Learning Engineer – Pre Training

• 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 – Pre Training

Machine Learning Engineer – Kernels

Machine Learning Engineer – Post Training