EnCharge AI
11 - 50 employees
Founded 2022
🤖 Artificial Intelligence
🔧 Hardware
🤝 B2B
💰 $100M Series B - EnCharge AI on 2025-02
Artificial Intelligence • Hardware • B2B
EnCharge AI is a company that develops analog in-memory computing hardware and complementary software to accelerate on-device and edge-to-cloud AI workloads. Their technology includes the EN100 analog AI accelerator and other form factors (chiplets, ASICs, PCIe cards) designed to deliver much higher energy efficiency, compute density, and lower total cost of ownership for inference compared with conventional GPUs and digital accelerators. EnCharge emphasizes sustainability, data privacy through local processing, and deployment for enterprise and developer customers seeking efficient, scalable AI computation outside traditional cloud infrastructure.
Research Engineer, AI Models
Job not on LinkedIn
🕒 June 26
Python
PyTorch
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EnCharge AI
11 - 50 employees
Founded 2022
🤖 Artificial Intelligence
🔧 Hardware
🤝 B2B
💰 $100M Series B - EnCharge AI on 2025-02
Artificial Intelligence • Hardware • B2B
EnCharge AI is a company that develops analog in-memory computing hardware and complementary software to accelerate on-device and edge-to-cloud AI workloads. Their technology includes the EN100 analog AI accelerator and other form factors (chiplets, ASICs, PCIe cards) designed to deliver much higher energy efficiency, compute density, and lower total cost of ownership for inference compared with conventional GPUs and digital accelerators. EnCharge emphasizes sustainability, data privacy through local processing, and deployment for enterprise and developer customers seeking efficient, scalable AI computation outside traditional cloud infrastructure.
📋 Description
• Research and implement state-of-the-art techniques to accelerate AI inference • Partner closely with hardware, compiler, and quantization teams • Build profiling tools and comprehensive benchmarking frameworks • Build robust fine-tuning workflows for modern AI models
🎯 Requirements
• 5+ years of experience in ML research, applied ML, or ML systems • Strong fundamentals in Python and PyTorch • Hands-on experience with modern AI models (transformers, diffusion models, or other generative architectures) • Experience fine-tuning large models and building training/evaluation pipelines • Deep understanding of transformers, attention mechanisms, & optimization techniques • Comfort reading and implementing techniques from research papers
🏖️ Benefits
• Health insurance • Flexible work arrangements • Professional development
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