Job Description

Quadric has created an innovative general purpose neural processing unit (GPNPU) architecture. Quadric's co-optimized software and hardware is targeted to run neural network (NN) inference workloads in a wide variety of edge and endpoint devices, ranging from battery operated smart-sensor systems to high-performance automotive or autonomous vehicle systems. Unlike other NPUs or neural network accelerators in the industry today that can only accelerate a portion of a machine learning graph, the Quadric GPNPU executes both NN graph code and conventional C++ DSP and control code.

Role:
You will join the data science team for internship focused on model optimization for Quadric's custom GPNPU architecture. Working alongside a senior data scientist mentor, you will contribute to quantization library and/or numerical accuracy testing/debugging infrastructure.

Responsibilities:

• Run and contribute to new quantization workflows on vision and language models under mentor guidance.
• Build calibration datasets and tooling to visualize per-layer error and distribution statistics for debugging.
• Contribute to numerical accuracy testing infrastructure, numerical validation debug tooling of neural networks, and quantization library.

Requirements

• Currently pursuing or recently graduated with a B.S., M.S., or Ph.D. in CS, EE, Applied Math, or a related field.
• Solid Python skills and comfort with PyTorch (or TensorFlow), NumPy, and basic data-viz tools (Matplotlib/Plotly).
• Coursework or project experience in machine learning; familiarity with CNNs and/or Transformers.
• Curiosity about quantization, numerical representation, fixed-point arithmetic, or low-level performance.
• Ability to read a research paper and discuss the core ideas.
• Bonus: prior exposure to quantization, model compression, or any of PyTorch FX/PTQ/QAT, TF-Lite, ONNX-Runtime, TVM, or MLIR Quant.
• Bonus: any hands-on experience with embedded systems, DSPs, GPUs, or other accelerators.

Benefits

At Quadric, we value Integrity, Humility, and Happiness. What we expect from one another is simple and clear: Initiative, Collaboration, and Completion. We are a collaborative team focused on building something extraordinary in the edge computing space. 

The hourly rate for this temporary internship position is $45.00/hour to $60.00/hour. The actual rate offered will depend on a number of factors, including the specific level of the role, years and depth of relevant experience and education, technical skills and competencies, and work location. 

Quadric interns receive hands-on experience working alongside industry experts in AI and semiconductor technology, with access to mentorship and meaningful project ownership from day one.

Founded in 2016 and based in downtown Burlingame, California, Quadric is building the world’s first supercomputer designed for the real-time needs of edge devices. Quadric aims to empower developers in every industry with superpowers to create tomorrow’s technology, today. The company was co-founded by technologists from MIT and Carnegie Mellon, who were previously the technical co-founders of the Bitcoin computing company 21.

Quadric is proud to be an equal opportunity employer. We are committed to creating an inclusive environment where people from all backgrounds can do their best work. We consider all qualified applicants without regard to race, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.

If this role resonates with you, we encourage you to apply even if your experience does not perfectly match every qualification. We value potential, curiosity, and a willingness to learn just as much as direct experience. Skills and growth come in many forms, and we would love to hear your story.

By submitting an application, you acknowledge that Quadric will collect and process your personal information as part of the hiring process. Please review our Privacy Policy to understand how we handle your data.

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