Model Compression & Quantization

For engineers focusing on inference and model optimization, this area tests your mathematical maturity and your ability to fit state-of-the-art models onto edge devices.

Be ready to go over:

• Quantization Schemes – The mechanics of converting 32-bit floating-point weights and activations to 8-bit or 4-bit integers (INT8/INT4).
• Calibration Methods – Using MinMax, Entropy (KL divergence), or Mean Squared Error (MSE) to determine optimal quantization scales.
• Pruning Strategies – Identifying redundant channels or weights and removing them systematically while preserving model accuracy.
• Advanced concepts (less common) – Mixed-precision quantization, handling non-linear activation functions (like Sigmoid or GeLU) in integer-only pipelines, and quantization-aware fine-tuning.

Example questions or scenarios:

• "Walk through the mathematics of quantizing a tensor. How do you handle clipping when outliers skew the dynamic range?"
• "If a quantized model suffers a severe drop in accuracy, what systematic steps do you take to isolate the problematic layer?"
• "How would you design a pruning pipeline that ensures the pruned model matches the structured block requirements of a vector processor?"

As an AI Engineer at quadric, your day-to-day work will bridge the gap between abstract machine learning research and physical hardware execution. You will spend your time writing high-performance code, analyzing execution profiles, and collaborating with cross-functional teams to make AI running on the edge incredibly fast and efficient.

You will be responsible for mapping complex neural network architectures onto the Chimera GPNPU software stack. This involves importing models from standard formats like ONNX, optimizing their computational graphs, and ensuring that every operator is lowered to highly efficient machine instructions. If a model uses a novel operator that the compiler does not yet support, you will design and implement a custom C++ kernel to execute that operator with maximum performance.

Collaboration is a core part of the culture at quadric. You will work closely with the hardware design team to provide feedback on architectural bottlenecks, helping to shape the next generation of silicon based on real-world model workloads. You will also collaborate with product and application teams to help customers integrate quadric chips into their final products, solving complex integration challenges under tight performance budgets.

To be competitive for an AI Engineer position at quadric, you must possess a strong foundation in computer science, computer architecture, and machine learning systems. The team values practical, demonstrable skills over theoretical credentials.

Technical Skills

• Programming Languages – Expert-level proficiency in C++ (specifically modern C++14/17/20) for kernel and systems development, and Python for model manipulation and scripting.
• Machine Learning Frameworks – Deep familiarity with PyTorch, TensorFlow, and the ONNX ecosystem.
• ML Compilers & Runtimes – Hands-on experience with Apache TVM, LLVM, OpenXLA, or proprietary compiler toolchains.
• Hardware Knowledge – Solid understanding of computer architecture, including SIMD/vector processing, cache hierarchies, DMA, and memory-mapped I/O.

Experience & Education

• Must-have skills – A Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field, combined with a strong portfolio of low-level optimization or machine learning systems work.
• Nice-to-have skills – Experience writing CUDA kernels, working with embedded systems (RTOS or embedded Linux), or participating in open-source compiler projects like TVM.

Q: How deep does my hardware knowledge need to be if I am applying for an Inference role? A: While you do not need to be a silicon designer, you must understand computer architecture basics. You should know how memory bandwidth, cache misses, and arithmetic intensity affect software execution, as these factors dictate how you optimize models.

Q: What is the primary coding language used during the interviews? A: For AI Kernel Engineer roles, the coding interviews are almost exclusively in C++. For AI Inference and AI Applications roles, you may use Python for model manipulation and high-level logic, but you should still expect to discuss low-level C++ concepts.

Q: Does quadric support hybrid or remote work for these engineering roles? A: Because these roles involve working closely with custom silicon development boards, testing hardware, and physical laboratory equipment, presence at the Burlingame, CA office is typically required. The team operates primarily in a hybrid or on-site model depending on the specific hardware access requirements of your role.

Q: What is the typical timeline from the first technical screen to an offer? A: The entire process generally takes between three to five weeks. quadric values thoroughness and candidate experience, so you will receive consistent communication and feedback at each stage of the process.

To stand out during your interview process at quadric, you must demonstrate that you can think like both a software engineer and a hardware architect.

• Focus on the "Why" behind optimization: When discussing a past project or solving an interview problem, never just state what you optimized. Explain why you chose that approach. For example, clarify whether you were trying to solve a memory bandwidth bottleneck or a compute latency issue.
• Master the basics of TVM and ONNX: Even if you haven't used them extensively, understand how they work conceptually. Know how an ONNX graph is structured and how a compiler lowers high-level operators into target-specific code.
• Be comfortable with ambiguity: Edge AI is a rapidly evolving field. You may be asked how to optimize a brand-new model architecture that has never been run on an NPU before. Show that you can break down a complex, novel problem into manageable, first-principles components.

An AI Engineer position at quadric represents an exceptional opportunity to shape the future of edge computing. By working on the Chimera GPNPU software stack, you will solve some of the most challenging problems in the industry, enabling complex neural networks to run efficiently on low-power devices. The work you do here will directly impact real-world applications in automotive, robotics, smart cities, and consumer electronics.

As you prepare for your interviews, focus your efforts on mastering low-level performance optimization, the mathematics of model compression, and the architectural principles of machine learning compilers. Practice writing clean, efficient C++ code that respects memory boundaries, and be ready to articulate your design choices clearly.

The compensation ranges reflect quadric's commitment to attracting top-tier engineering talent. Base salaries for full-time positions span a wide band to accommodate different seniority levels, from mid-level engineers to principal architects, while interns are compensated with highly competitive hourly rates. Leverage this data to align your expectations and highlight your specialized skills during the final stages of the process.

To gain deeper insights, review actual interview questions, and access additional preparation resources tailored for hardware-aware software roles, explore the community-driven databases on Dataford. With focused preparation and a strong grasp of first-principles engineering, you are well-positioned to succeed in the interview process and join the pioneering team at quadric.