Mindbeam AI
1 - 10 employees
Founded 2024
🤖 Artificial Intelligence
🤝 B2B
Artificial Intelligence • B2B
Mindbeam AI is a company building Litespark, an ultra-fast LLM pretraining framework that accelerates training and inference for generative AI applications. Its software improves throughput on existing GPU hardware with zero code changes, is compatible with industry-standard ML frameworks like PyTorch, and claims to reduce pretraining time from months to days while lowering energy consumption and costs (up to ~81% energy savings in cited workloads). Mindbeam targets enterprise and B2B customers seeking scalable, efficient AI infrastructure for large language model development.
Machine Learning Engineer – Pre Training
Job not on LinkedIn
🕒 May 26
🇺🇸 United States – Remote
💵 $150k - $190k / year
⏰ Full Time
🟢 Junior
🟡 Mid-level
🤖 Machine Learning Engineer
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Mindbeam AI
1 - 10 employees
Founded 2024
🤖 Artificial Intelligence
🤝 B2B
Artificial Intelligence • B2B
Mindbeam AI is a company building Litespark, an ultra-fast LLM pretraining framework that accelerates training and inference for generative AI applications. Its software improves throughput on existing GPU hardware with zero code changes, is compatible with industry-standard ML frameworks like PyTorch, and claims to reduce pretraining time from months to days while lowering energy consumption and costs (up to ~81% energy savings in cited workloads). Mindbeam targets enterprise and B2B customers seeking scalable, efficient AI infrastructure for large language model development.
📋 Description
• 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.
🎯 Requirements
• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or related field—or equivalent experience. • 2+ years of experience with large-scale model training and distributed systems. • Strong coding skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, JAX). • Experience with GPU scheduling, memory optimization, and parallelism strategies. • Comfort with containerized and orchestrated environments (Docker/Kubernetes). • Understanding of high-performance computing and networking bottlenecks.
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