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 – Kernels
Job not on LinkedIn
🕒 May 21
🏄 California – 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
• 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.
🎯 Requirements
• Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or related field—or equivalent experience. • 2+ years of experience in GPU programming, parallel computing, or systems-level optimization. • Strong coding skills in C++, CUDA, or similar languages. • Familiarity with ML frameworks and their low-level backends. • Experience optimizing workloads for distributed and heterogeneous compute environments. • Comfort with profiling tools and performance diagnostics.
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