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EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.
About the Role
EnCharge AI is seeking a highly skilled and experienced AI Compiler Engineer to spearhead the efforts in developing and optimizing graph compilers tailored to cutting-edge AI and ML workloads. You will collaborate with hardware architects, and AI researchers to enhance performance, optimize computation graphs, and enable efficient model deployment on EnCharge’s Inference Accelerators.
Responsibilities
Qualifications
EnchargeAI is an equal employment opportunity employer in the United States.
Ready to apply?
Apply to EnCharge AIShare this job
EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.
About the Role
EnCharge AI is looking for an experienced AI Research Engineer to optimize deep learning models for deployment on edge AI platforms. You will work on model compression, quantization strategies, and efficient inference techniques to improve the performance of AI workloads.
Responsibilities
Research and develop quantization-aware training (QAT) and post-training quantization (PTQ) techniques for deep learning models.
Implement low-bit precision optimizations (e.g., INT8, BF16).
Design and optimize efficient inference algorithms for AI workloads, focusing on latency, memory footprint, and power efficiency.
Work with frameworks such as PyTorch, ONNX Runtime, and TVM to deploy optimized models.
Analyze accuracy trade-offs and develop calibration techniques to mitigate precision loss in quantized models.
Collaborate with hardware engineers to optimize model execution for edge devices, and NPUs.
Contribute to research on knowledge distillation, sparsity, pruning, and model compression techniques.
Benchmark performance across different hardware and software stacks.
Stay updated with the latest advancements in AI efficiency, model compression, and hardware acceleration.
Qualifications
Master’s or Ph.D. in Computer Science, Electrical Engineering, or a related field.
Strong expertise in deep learning, model optimization, and numerical precision analysis.
Hands-on experience with model quantization techniques (QAT, PTQ, mixed precision).
Proficiency in Python, C++, CUDA, or OpenCL for performance optimization.
Experience with AI frameworks: PyTorch, TensorFlow, ONNX Runtime, TVM, TensorRT, or OpenVINO.
Understanding of low-level hardware acceleration (e.g., SIMD, AVX, Tensor Cores, VNNI).
Familiarity with compiler optimizations for ML workloads (e.g., XLA, MLIR, LLVM).
EnchargeAI is an equal employment opportunity employer in the United States.
Ready to apply?
Apply to EnCharge AIShare this job
EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.
About the Role
EnCharge AI is seeking an LLM Inference Deployment Engineer to optimize, deploy, and scale large language models (LLMs) for high-performance inference on its energy efficient AI accelerators. You will work at the intersection of AI frameworks, model optimization, and runtime execution to ensure efficient model execution and low-latency AI inference.
Responsibilities
Qualifications
EnchargeAI is an equal employment opportunity employer in the United States.
Ready to apply?
Apply to EnCharge AICookies & analytics
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