quadric AI Engineer interview questions & guide 2026

The interview process at quadric is highly technical and tailored to your specific domain expertise. While questions may vary depending on whether you are interviewing for an Inference, Kernel, or Applications track, they are designed to evaluate your fundamental understanding of machine learning math, low-level system design, and hardware-aware programming.

AI Inference & Model Optimization

This category focuses on your ability to take high-level deep learning models (from PyTorch or TensorFlow) and prepare them for efficient execution on custom silicon.

• Explain the mathematical difference between symmetric and asymmetric quantization. How do you handle zero-point calibration?
• What are the primary trade-offs between structured and unstructured pruning? How does hardware architecture influence your choice?

Preparing for an interview at quadric requires a balanced approach. You must be ready to demonstrate both high-level algorithmic thinking and low-level execution discipline.

Role-Related Knowledge – You must deeply understand the software stack of your chosen domain. For kernel engineers, this means mastering C++ and computer architecture; for inference engineers, it means mastering quantization, pruning, and compiler tools like TVM.

Problem-Solving & System Design – Interviewers want to see how you approach resource constraints. You should practice structuring solutions where memory, power, and latency are highly restricted, showing that you can make pragmatic design trade-offs.

Communication & Collaboration – Because quadric builds both hardware and software, you will frequently collaborate across team boundaries. You must be able to explain complex software concepts to hardware architects and vice versa, demonstrating a collaborative, ego-free approach to engineering.

The interview process at quadric is rigorous, transparent, and designed to evaluate your practical engineering capabilities. The company values hands-on coding and architectural depth over abstract brainteasers, ensuring that the evaluation matches the actual demands of the job.

The journey begins with an initial technical screening, typically conducted by a hiring manager or senior engineer. This conversation focuses on your background, your experience with deep learning frameworks or low-level systems, and a few light technical questions to calibrate your domain depth. Following a successful screen, you will progress to a series of deep-dive technical interviews. These sessions include live coding challenges (focusing on C++ or Python depending on the track), system design discussions, and deep dives into model compilation or hardware-software co-design.

This diagram outlines the typical progression from your initial application to the final decision. Candidates should expect a highly structured sequence that tests both theoretical knowledge and practical execution. Use this timeline to pace your preparation, ensuring you dedicate ample time to both coding syntax and system-level architectural concepts before the intensive onsite rounds.

To succeed at quadric, you must perform exceptionally well across several core competency areas. The engineering team evaluates candidates based on their ability to write production-grade code that respects hardware limitations.

C++ and Low-Level Performance Optimization

This evaluation area is central to the AI Kernel Engineer and AI Applications Engineer tracks. The interviewers want to see if you can write code that maximizes the throughput of the processor while minimizing memory overhead.

Be ready to go over:

• Memory Hierarchy Management – How to leverage registers, L1/L2 cache, SRAM, and DRAM effectively to prevent execution stalls.

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.