Lead the end-to-end implementation and long-term ownership of a new HRIS platform, including system selection and data migration. Optimize HR processes and reporting capabilities while ensuring data integrity and integration with other business systems.
EnCharge AI
10 Remote Job Openings at EnCharge AI
Principal SOC Physical Verification & Integration Specialist
EnCharge AI
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Full Time
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a month ago
EnCharge AI
Drive the full-chip physical assembly and signoff of large-scale SOCs from floorplan to tapeout. Architect hierarchical physical verification flows and resolve complex bottlenecks to ensure optimal power, performance, and area.
Staff / Senior Staff CAD & Methodology Engineer (Digital Implementation & Signoff)
EnCharge AI
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Full Time
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a month ago
EnCharge AI
Develop and optimize automated RTL-to-GDSII and timing signoff flows using Cadence Innovus and Tempus. Collaborate with Physical Design and STA leads to reduce turnaround time and improve PPA targets for AI accelerators.
Research and implement state-of-the-art techniques to accelerate AI inference and optimize model quality on custom silicon. Build fine-tuning pipelines and benchmarking frameworks to characterize tradeoffs between latency, throughput, and power consumption.
The role involves architecting and implementing optimizations for AI model execution on graph compilers to maximize hardware utilization and reduce latency. You will collaborate with hardware architects and researchers to convert high-level AI models into efficient intermediate representations for inference accelerators.
Define and develop the specifications and micro-architecture for key NPU modules, including in-memory compute and memory orchestration units. Collaborate with hardware and software teams to optimize AI accelerator performance for workloads like LLMs and CNNs.
The Senior Emulation Engineer will set up and maintain emulation platforms while adapting SoC designs for validation. They will collaborate with design and software teams to debug architectures, optimize workloads, and support early software bring-up.
The AI Compiler Engineer will architect, design, and implement optimizations for AI model execution on graph compilers. They will collaborate with hardware architects and AI researchers to enhance performance and enable efficient model deployment.
The LLM Inference Deployment Engineer will optimize, deploy, and scale large language models for high-performance inference on energy-efficient AI accelerators. Responsibilities include utilizing inference runtimes and optimizing model execution for low-latency AI inference.
The AI Research Engineer will research and develop quantization techniques for deep learning models and implement optimizations for efficient inference algorithms. Collaboration with hardware engineers is essential to optimize model execution for edge devices.
