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

• Architect, design, and implement optimizations for AI model execution on graph compilers to improve performance, reduce latency, and maximize hardware utilization.
• Work closely with ML researchers, hardware engineers, and software developers to design and deploy AI models, understanding and addressing hardware-specific challenges.
• Work on performance optimizations for neural network models, such as layer fusion, operator fusion, and graph-level transformations.
• Develop compiler optimizations and passes that convert high-level AI models (e.g., from TensorFlow, PyTorch) into intermediate representations (IR).
• Implement parsing, semantic analysis, and IR generation for deep learning frameworks.
• Research and integrate the latest advancements in compiler design, ML model optimizations, and hardware acceleration into graph compilers.
• Provide leadership, mentorship, and technical guidance to a team of engineers focused on graph compiler optimizations. 

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (Ph.D. preferred).
• 3+ years in compiler development, with a strong focus on AI or ML graph compilers.
• Proficiency in AI graph compiler frameworks (e.g., MLIR, Torch-FX)
• Solid background in hardware architectures (e.g., GPUs, TPUs, ASICs) and optimization techniques such as fusion, quantization, and tiling.
• Familiarity with neural networks operators and code generation.
• Strong understanding of intermediate representations, code parsing, and semantic analysis in compiler design.
• Proficiency in C++, Python, or other programming languages commonly used in compiler development.
• Open-source contributions to AI software frameworks and libraries is a plus
• Demonstrated experience leading and mentoring engineering teams with successful project delivery. 

EnchargeAI is an equal employment opportunity employer in the United States.

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 Position:

The Silicon Operations Manager is responsible for driving operational activities from product definition and production ramp to sustained production. The individual will drive new product introduction activities, operational readiness for production and product lifecycle management. The silicon Operations Manager will be responsible for development, qualification and manufacturing ramp up of the company’s silicon products. The Silicon Operations Manager will report to the COO.

Responsibilities:

• Coordinate transitioning tests from the lab environment to the production environment
• Develop, debug, qualify, and deploy test programs to meet and exceed evolving customer needs and expectations
• Drive yield optimization activities and improvements
• Control the company’s RMA procedures and process.
• Work with marketing teams to provide a first line of defense for yield loss, RMA and customer returns.
• Drive failure analysis activities, investigating and solving complex issues in a fast-paced test department.
• Designing experiments and analyzing data to root cause and debug product failures
• Provide primary interface between the company and external semiconductor vendors (OSATs, foundries etc.)
• Lead cross-functional teams in operations, engineering, and quality assurance to meet production targets and KPIs.
• Collaborate with Supply Chain and Planning to develop and execute efficient production schedules based on customer demand and inventory goals.
• Develop, manage, and execute qualification activities, including prototyping parts, engineering samples and qualification builds.
• Coordinate Supply Chain Planning, Procurement and Sales Operations to support product development and manufacturing ramp‑up.
• Oversee Bill of Materials (BOM) accuracy and SKU management
• Maintain and update internal systems to reflect NPI/engineering demand, assembly instructions, build requirements, and engineering sample plans.
• Lead continuous improvement initiatives to advance operational modeling, automation, and cost‑reduction opportunities.

Qualifications:

• 10+ years’ experience in Semiconductor and high-tech manufacturing
• Deep understanding of semiconductor manufacturing process from semiconductor manufacturing to production test.
• Strong background in semiconductor development and product lifecycle management.
• Experience with FMA activities and failure analysis in the lab and production test environments
• Experience in Semiconductor Parametric & Functional Test and Characterization
• Experience in manufacturing metrics and problem-solving in a manufacturing environment
• Excellent interpersonal skills, energetic, motivated, and self-driven
• Outstanding communication skills - both written and verbal
• Demonstrated ability to communicate well with all levels of the organization and experience in working with external constituencies
• Strong organizational skills; demonstrated ability to manage multiple tasks simultaneously, and able to react to shifting priorities to meet business needs
• Demonstrated ability to meet deadlines and commitments
• Familiar with 6-sigma, DOE, FMEA

The salary range for this position is $125,000 to $200,000 per year. Actual compensation offered will be determined based by factors as job-related knowledge, skills and experience.

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.

Principal DFT Engineer

Job Description:
Developing silicon for AI Computing isn't just about speed; it’s about balancing high-performance data processing with extreme power efficiency and reliability in remote environments.

As a Principal DFT(Design for Test) Engineer, you will lead our testing strategy, ensuring the manufacturing quality, reliability, and test efficiency of complex, high-performance AI accelerators.

Key Responsibilities:
Architectural Leadership: Define and implement the end-to-end DFT architecture for complex SoCs, including Hierarchical DFT, Scan compression, Boundary Scan and MBIST.

Edge-Specific Reliability: Develop strategies for In-System Test (IST) and power-on self-test (POST) to ensure chip health in remote edge data centers.

Implementation & Flow: Oversee scan insertion, ATPG (Stuck-at, Transition, Path Delay), and Memory/Logic BIST.

Cross-Functional Synergy: Collaborate with Design, Physical Design, and Yield teams to ensure high test coverage while minimizing area overhead and power impact as well as timing analysis.

Post-Silicon Validation: Lead the bring-up and debug phase on ATE (Automated Test Equipment) to root-cause silicon failures and optimize test time.

Technical Requirements:
Experience: 10+ years in DFT, with at least 2 years in a leadership or principal role.

Tools: Mastery of industry-standard tools (e.g., Synopsys TestMAX, Siemens/Mentor Tessent, or Cadence Genus/Modus).

Memory & Logic Test: Deep expertise in MBIST (Memory Built-In Self-Test) with repair capabilities, SCAN, IJTAG (IEEE 1687) and boundary scan (IEEE 1149.1/6).

Advanced Nodes: Proven track record with FinFET nodes (7nm, 5nm, or below).

Low Power: Experience managing DFT in multi-voltage/power-gated designs—crucial for edge efficiency.

The salary range for this position is $180,000 to $220,000 per year. Actual compensation offered will be determined based by factors as job-related knowledge, skills and experience.

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.

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 Position:

Hardware Technical Writer (Part-time)

The Hardware Technical Writer will report to the Chief Operating Officer and is a Remote Virtual, 1099 contract position.

Responsibilities:

• Collaborate with internal Architecture, Analog and Digital Design Engineering Teams to gather information for technical documents
• Writing design specifications, user manuals, and datasheets for AI Hardware
• Reviewing and revising architecture specifications, conference papers, integration guides
• Produce Government reports, Integration Guides, SDK User Manuals
• Translates complex ASIC or VLSI concepts into clear and concise documentation for engineers and customers

Qualifications:

• 5+ years experience in Technical Writing
• Experience with writing technical design specifications, user manuals, and datasheets
• Experience in collaborating with Engineering teams to gather technical information
• Experience in Technical Writing for ASIC or VLSI teams
• Work experience in Technical Writing for Artificial Intelligence companies is a plus
• B.S. in Electrical Engineering or similar field
• Clear communication with engineers to ensure accuracy
• Experience in tools such as Markdown, LaTeX, Microsoft Word

This is a 1099 contractor role. With an estimated 10 hours a week scope. The pay range for this position is $50 to $55 USD per hour if located in US. Actual compensation offered will be determined based by factors as job-related knowledge, skills and experience.

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 AI Runtime Engineer to develop and optimize the execution stack for our next-generation AI accelerator. In this role, you will work on low-latency, high-performance runtime software that enables efficient execution of deep learning models on specialized hardware. You will collaborate with hardware, compiler, and AI framework teams to deliver optimized AI inference and training performance across cloud and edge environments. 

Responsibilities

• Develop and optimize the AI runtime software stack for executing deep learning workloads on AI accelerators.
• Implement task scheduling, memory management, and kernel execution strategies for efficient computation.
• Optimize data movement between host and device using PCIe, DMA, shared memory.
• Design and implement high-performance APIs for AI Inference frameworks such as OpenVino, ONNX Runtime, vLLM
• Work on graph execution optimizations, including kernel fusion, pipelining, tensor tiling, and caching.
• Integrate runtime components with AI compilers (LLVM, MLIR, XLA, TVM) for optimized execution.
• Ensure scalability and reliability of the AI runtime for cloud-based and edge AI deployments. 

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.
• 3+ years of experience in developing low-level runtime software for AI accelerators, GPUs, or HPC systems.
• Strong proficiency in C/C++ and low-level systems programming.
• Deep understanding of task scheduling, concurrency, and memory hierarchy.
• Experience with hardware-aware optimizations and dataflow architectures.
• Familiarity with deep learning execution frameworks (ONNX Runtime, TensorRT, TVM, OpenVINO).
• Experience with low-latency, high-throughput workload execution for AI models.
• Strong debugging and profiling skills for optimizing AI execution performance.
• Exposure to AI model deployment pipelines (Triton, TensorFlow Serving). 

EnchargeAI is an equal employment opportunity employer in the United States.

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

• Deploy and optimize LLMs (GPT, LLaMA, Mistral, Falcon, etc.) post-training from libraries like HuggingFace
• Utilize inference runtimes such as ONNX Runtime, vLLM for efficient execution.
• Optimize batching, caching, and tensor parallelism to improve LLM scalability in real-time applications.
• Develop and maintain high-performance inference pipelines using Docker, Kubernetes, and other inference servers. 

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field.
• Experience in LLM inference deployment, model optimization, and runtime engineering.
• Strong expertise in LLM inference frameworks (PyTorch, ONNX Runtime, vLLM, TensorRT-LLM, DeepSpeed).
• In-depth knowledge of the Python programming language for model integration and performance tuning.
• Strong understanding of high-level model representations and experience implementing framework-level optimizations for Generative AI use cases
• Experience with containerized AI deployments (Docker, Kubernetes, Triton Inference Server, TensorFlow Serving, TorchServe).
• Strong knowledge of LLM memory optimization strategies for long-context applications.
• Experience with real-time LLM applications (chatbots, code generation, retrieval-augmented generation). 

EnchargeAI is an equal employment opportunity employer in the United States.

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 Device Driver Engineer to design and implement high-performance driver stack for our cutting-edge AI accelerator hardware. In this role, you will work closely with hardware, firmware, and AI software teams to develop low-latency, high-bandwidth communication between the host system and AI accelerator.

Responsibilities

• Develop, optimize, and maintain Linux/Windows PCIe device drivers for AI accelerators.
• Implement low-level hardware interactions, DMA, memory management, and interrupt handling.
• Work on driver optimizations to reduce latency and improve throughput for AI workloads.
• Debug and troubleshoot PCIe protocol, kernel panics, crashes, and performance bottlenecks.
• Collaborate with hardware, firmware, and AI software teams to define driver interfaces.
• Ensure compliance with PCIe standards (Gen4/Gen5), SR-IOV, BAR memory mapping, and IOMMU.
• Support virtualization (VFIO, SR-IOV, DPUs) and containerized environments (Kubernetes, Docker, etc.).
• Develop tools for profiling, debugging, and monitoring driver performance.
• Contribute to open-source kernel modules if applicable. 

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.
• 3+ years of experience in device driver development for Linux and/or Windows.
• Strong experience with PCIe-based hardware, including BAR regions, DMA, interrupts, and MMIO.
• Proficiency in C/C++ and kernel-mode programming (Linux Kernel, Windows WDDM/WDF/MCDM).
• Experience with AI-specific accelerators (e.g., GPUs, NPUs, TPUs) is a plus.
• Knowledge of low-level debugging tools (gdb, perf, ftrace, dmesg, PCIe analyzers). 
• Understanding of multi-threading, synchronization, and memory management in kernel space.
• Familiarity with high-performance AI/ML workloads is a plus.
• Experience in hypervisor interactions, VFIO, and passthrough solutions.
• Knowledge of secure boot, firmware updates, and trusted execution environments (TEE). 

 EnchargeAI is an equal employment opportunity employer in the United States.

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 Embedded SW Engineer to develop the firmware for our Edge AI processors. The candidate must possess an excellent understanding of computer architecture and operating system concepts including, but not limited to, memory management, virtualization and PCIe address space. The role includes designing and developing the core Firmware for various parts of the SOC. The candidate must possess strong communication skills to interface with Runtime, Architecture and H/W teams. 

Responsibilities

• Develop the critical pieces of EAI Firmware used to deploy inference jobs on EAI processors
• Validate different IP blocks on the SOC
• Evaluate and integrate third-party device drivers to interface with EnCharge’s SW stack
• Work closely with the Runtime, Hardware and Architecture teams define the driver architecture 

Qualifications

• Bachelors in EE/CS
• Advanced programming skills in C/C++ for operating system kernel & systems development
• Understanding of RISC-V architecture is a plus
• Exposure to PCIe BAR and IOMMU architecture
• Exposure to virtualization and hypervisor technologies
• Deep understanding of operating systems concepts, data structures, x86-64 and accelerator architectures
• Experience with low-level debug tools as well as emulators and simulators
• Experience running, analyzing, and tuning system performance benchmarks
• Excellent verbal and written communication skills 

EnchargeAI is an equal employment opportunity employer in the United States.