👋 About the Team
The Core Research team leads the development of core technologies behind NetsPresso’s modules, including Trainer, Compressor, and Converter. We focus on training HW-aware AI models, developing new lightweighting techniques, and enabling compatibility across a variety of edge devices.
📌 What You’ll Do at This Position
In this role, you will research and productize quantization—one of the core technologies within the Converter module. You’ll contribute to optimizing on-device AI models and developing proprietary quantization techniques that enhance the performance of NetsPresso's deployment pipeline.
📙Achievements & Relevant Materials
✅ Key Responsibilities
- Research and Productization of Quantization Techniques
- Post-training quantization and quantization-aware training for various deep learning models
- Generative AI (such as stable diffusion, large language models, etc.), Computer Vision (including classification, object detection, segmentation, etc.), and other fields
- Model Optimization and Conversion for On-device AI
- Utilizing frameworks like TensorRT, TensorFlow Lite, and others to optimize and convert models for on-device AI applications
✅ Requirements
- 2+ years of relevant experience or equivalent expertise
- Degree in Computer Engineering, Electrical Engineering, or a related field
- Experience optimizing deep learning models using PyTorch
- Master’s degree holders must have at least 2 years of experience
- No restrictions on international travel
✅ Pluses
- Experience in quantization, compression, or kernel-level optimization
- Experience optimizing models using TensorRT, TensorFlow Lite, ArmNN, or similar frameworks
- Experience porting low-precision models to embedded devices
- Familiarity with ML/DL compilers such as TVM, Glow, nGraph, or XLA
- Published papers related to model optimization
✅ Hiring Process
- Document Screening → Screening Interview → Coding Test/Assignment → 1st Interview → 2nd Interview
(Additional tasks may be included between the steps)
🤓 A Message from the Team
We’re looking for someone who is passionate about new technology and turning ideas into working solutions. This role is more than just research—you’ll be developing original quantization methods that directly power NetsPresso services. Because our modules are deeply interconnected, we value active communication and a proactive mindset. If you enjoy diving deep into complex technical problems and growing alongside a collaborative team, you’ll thrive here.
Please Check Before Applying! 👀
- This job posting is open continuously, and it may close early upon completion of the hiring process.
- Please ensure that sensitive personal information, such as salary details, ID number etc., is not included in your resume.
- Providing false information in the submitted materials may result in the cancellation of the application.
- Please be aware that references will be checked before finalizing the hiring decision.
- Compensation will be discussed separately upon successful completion of the final interview.
- There will be a probationary period after joining, and there will be no discrimination in the treatment during this period.
Veterans and individuals with disabilities will receive preferential treatment in accordance with relevant regulations.
🔎 Helpful materials
👋 About the Team
The Core Research team leads the development of core technologies behind NetsPresso’s modules, including Trainer, Compressor, and Converter. We focus on training HW-aware AI models, developing new lightweighting techniques, and enabling compatibility across a variety of edge devices.
📌 What You’ll Do at This Position
In this role, you will research and productize quantization—one of the core technologies within the Converter module. You’ll contribute to optimizing on-device AI models and developing proprietary quantization techniques that enhance the performance of NetsPresso's deployment pipeline.
📙Achievements & Relevant Materials
✅ Key Responsibilities
- Research and Productization of Quantization Techniques
- Post-training quantization and quantization-aware training for various deep learning models
- Generative AI (such as stable diffusion, large language models, etc.), Computer Vision (including classification, object detection, segmentation, etc.), and other fields
- Model Optimization and Conversion for On-device AI
- Utilizing frameworks like TensorRT, TensorFlow Lite, and others to optimize and convert models for on-device AI applications
✅ Requirements
- 2+ years of relevant experience or equivalent expertise
- Degree in Computer Engineering, Electrical Engineering, or a related field
- Experience optimizing deep learning models using PyTorch
- Master’s degree holders must have at least 2 years of experience
- No restrictions on international travel
✅ Pluses
- Experience in quantization, compression, or kernel-level optimization
- Experience optimizing models using TensorRT, TensorFlow Lite, ArmNN, or similar frameworks
- Experience porting low-precision models to embedded devices
- Familiarity with ML/DL compilers such as TVM, Glow, nGraph, or XLA
- Published papers related to model optimization
✅ Hiring Process
- Document Screening → Screening Interview → Coding Test/Assignment → 1st Interview → 2nd Interview
(Additional tasks may be included between the steps)
🤓 A Message from the Team
We’re looking for someone who is passionate about new technology and turning ideas into working solutions. This role is more than just research—you’ll be developing original quantization methods that directly power NetsPresso services. Because our modules are deeply interconnected, we value active communication and a proactive mindset. If you enjoy diving deep into complex technical problems and growing alongside a collaborative team, you’ll thrive here.
Please Check Before Applying! 👀
- This job posting is open continuously, and it may close early upon completion of the hiring process.
- Please ensure that sensitive personal information, such as salary details, ID number etc., is not included in your resume.
- Providing false information in the submitted materials may result in the cancellation of the application.
- Please be aware that references will be checked before finalizing the hiring decision.
- Compensation will be discussed separately upon successful completion of the final interview.
- There will be a probationary period after joining, and there will be no discrimination in the treatment during this period.
Veterans and individuals with disabilities will receive preferential treatment in accordance with relevant regulations.
🔎 Helpful materials