Viridan Group
Principal Recruitment Consultant at Viridan Group
Company Overview: Our client is a rapidly growing semiconductor innovator focused on developing ultra-low-power system-on-chip (SoC) solutions that enable intelligent, battery-powered devices across the globe. Their technology powers next-generation consumer, industrial, and IoT products that deliver advanced features while maintaining exceptional energy efficiency. With hundreds of millions of chips shipped worldwide, the company continues to push the boundaries of endpoint AI and sustainable computing.
This range is provided by Viridan Group. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $90,000.00/yr - $170,000.00/yr
Direct message the job poster from Viridan Group
This position can be based in Austin, TX or San Diego, CA, with an expectation of on-site presence five days per week.
Role Scope The Endpoint AI team focuses on enabling machine learning (ML) and deep learning (DL) model deployment across a family of power-optimized SoCs. Using advanced model compression and optimization techniques, the team brings sophisticated AI workloads to devices operating under tight compute, memory, and power constraints.
The ideal candidate will be passionate about efficient computing and comfortable working in a fast-paced, first-generation ("version zero") development environment—leveraging internal, open-source, and third-party tools to deliver robust AI capabilities for real-world applications.
Key Responsibilities
Design, refine, and deploy ML/DL models optimized for highly constrained embedded environments.
Apply state-of-the-art pruning, quantization, and distillation methods to balance performance, accuracy, and power efficiency.
Develop, train, and document models for inclusion in internal repositories and customer-facing AI libraries.
Collaborate with cross-functional hardware and software teams to ensure optimal model integration.
Contribute to technical publications, conferences, and community engagements to share best practices.
Required Skills & Experience
Hands-on experience with model compression techniques such as pruning, quantization, or knowledge distillation for CNNs or RNNs.
Domain experience in audio classification, speech recognition, computer vision, or time-series analysis, including feature extraction.
Strong proficiency with TensorFlow (TFLite, TFLite Micro) and/or PyTorch.
Familiarity with dataset creation, curation, and augmentation for embedded ML tasks.
Bachelor’s degree in Computer Science, Electrical Engineering, or related field (Master’s or PhD preferred).
Preferred Qualifications
Background in TinyML or on-device AI.
Experience developing for microcontroller or embedded C/C++ environments.
Knowledge of attention-based architecture compression.
Familiarity with model-to-binary compilers (IREE, MicroTVM, etc.).
Experience using ONNX, TOSA, JAX, LLVM, or MLIR.
Understanding of heterogeneous compute environments (CPU + NPU + DSP).
Seniority level Mid-Senior level
Employment type Full-time
Job function Engineering, Information Technology, and Research
Industries Research Services and Semiconductor Manufacturing
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This range is provided by Viridan Group. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $90,000.00/yr - $170,000.00/yr
Direct message the job poster from Viridan Group
This position can be based in Austin, TX or San Diego, CA, with an expectation of on-site presence five days per week.
Role Scope The Endpoint AI team focuses on enabling machine learning (ML) and deep learning (DL) model deployment across a family of power-optimized SoCs. Using advanced model compression and optimization techniques, the team brings sophisticated AI workloads to devices operating under tight compute, memory, and power constraints.
The ideal candidate will be passionate about efficient computing and comfortable working in a fast-paced, first-generation ("version zero") development environment—leveraging internal, open-source, and third-party tools to deliver robust AI capabilities for real-world applications.
Key Responsibilities
Design, refine, and deploy ML/DL models optimized for highly constrained embedded environments.
Apply state-of-the-art pruning, quantization, and distillation methods to balance performance, accuracy, and power efficiency.
Develop, train, and document models for inclusion in internal repositories and customer-facing AI libraries.
Collaborate with cross-functional hardware and software teams to ensure optimal model integration.
Contribute to technical publications, conferences, and community engagements to share best practices.
Required Skills & Experience
Hands-on experience with model compression techniques such as pruning, quantization, or knowledge distillation for CNNs or RNNs.
Domain experience in audio classification, speech recognition, computer vision, or time-series analysis, including feature extraction.
Strong proficiency with TensorFlow (TFLite, TFLite Micro) and/or PyTorch.
Familiarity with dataset creation, curation, and augmentation for embedded ML tasks.
Bachelor’s degree in Computer Science, Electrical Engineering, or related field (Master’s or PhD preferred).
Preferred Qualifications
Background in TinyML or on-device AI.
Experience developing for microcontroller or embedded C/C++ environments.
Knowledge of attention-based architecture compression.
Familiarity with model-to-binary compilers (IREE, MicroTVM, etc.).
Experience using ONNX, TOSA, JAX, LLVM, or MLIR.
Understanding of heterogeneous compute environments (CPU + NPU + DSP).
Seniority level Mid-Senior level
Employment type Full-time
Job function Engineering, Information Technology, and Research
Industries Research Services and Semiconductor Manufacturing
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