Member of Technical Staff - Edge AI Inference Engineer

The Member of Technical Staff - Edge AI Inference Engineer at Liquid AI is responsible for optimizing inference stacks for various edge hardware platforms. This involves understanding modern hardware architectures and performance optimization, effectively working with frameworks such as llama.cpp, Executorch, and TensorRT, and delivering optimized solutions for AI models. You'll work closely with the machine learning team to enhance model execution and ensure low latency in real-time AI applications.

Applicants for the Edge AI Inference Engineer position at Liquid AI should have extensive experience with inference on embedded hardware, proficiency in Python and PyTorch, and a strong understanding of CPU, NPU, and GPU architectures. Additionally, expertise in C++, Rust, or similar languages is beneficial, as is familiarity with cache-aware algorithms and mobile development.

An Edge AI Inference Engineer plays a critical role in product development at Liquid AI by optimizing machine learning model performance on embedded devices. By ensuring efficient execution of AI models tailored to various hardware architectures, they enable our products to achieve exceptional throughput and low latency, thereby enhancing the overall user experience with AI-powered solutions.

To succeed as a Member of Technical Staff - Edge AI Inference Engineer at Liquid AI, you should have a strong background in machine learning, proficiency in relevant programming languages, and the ability to work independently. Understanding hardware implications on performance is crucial, as is experience with optimizing low-level primitives. Additionally, you should have a passion for exploring new challenges in edge AI systems.

At Liquid AI, you can expect a collaborative and innovative work environment where creativity and technical expertise are highly valued. We emphasize team ownership and encourage self-motivated individuals to take initiative. Our culture fosters continuous learning and exploration, with opportunities to contribute to meaningful projects that advance the future of AI technology.

To optimize an AI model for edge devices, I'd start by profiling the model to identify bottlenecks, followed by streamlining the computation by leveraging optimized inference frameworks like TensorRT. I would ensure that the model memory usage is efficient and reduce its complexity without sacrificing performance, potentially exploring quantization techniques.

In my previous role, I worked extensively on optimizing low-level primitives for real-time AI applications, focusing on enhancing performance through memory-efficient algorithms and cache optimization. I'm proficient in tools like PyTorch for implementing these optimizations and have a good grasp of hardware architectures.

Cache hierarchies are critical for improving data access speeds during model execution. I understand how different levels of cache (L1, L2, L3) can be leveraged to keep frequently accessed data readily available, minimizing latency. My approach involves optimizing data structures to fit within these caches effectively.

I have experience with several optimization frameworks including llama.cpp, Executorch, and TensorRT. I've applied these frameworks to reduce model inference time considerably by implementing layer fusion and weight pruning, ultimately leading to smooth execution on edge devices.

I have extensive coding experience in Python, particularly in relation to machine learning libraries such as PyTorch. This expertise has empowered me to interface effectively with ML teams, develop prototypes quickly, and analyze model performance, directly translating to impactful AI solutions.

I ensure code quality by adhering to best practices, utilizing code reviews, and implementing unit tests during the development process. This attention to detail not only helps in maintaining efficiency but also ensures that optimal performance is consistently achieved during model execution.

One challenging problem I faced was processing speed bottlenecks on an embedded GPU. I had to profile the model and identified inefficient memory access patterns. By reorganizing the data flow and optimizing the model structure, I dramatically enhanced the processing speed and performance.

I have foundational experience in mobile development, particularly in deploying AI models on Android platforms. This involved optimizing models for lower computational power, ensuring they run efficiently without compromising user experience or model accuracy.

I would begin by analyzing the architecture of the existing inference stack, followed by assessing the requirements and constraints of the new AI model. After ensuring compatibility, I would proceed with performance tuning and integration testing to ensure seamless operation and optimal performance.

I stay updated by actively engaging with the AI community through forums, attending relevant workshops, and following leading researchers. I also regularly read scientific papers and blogs on emerging trends in AI and hardware optimization to ensure my skills are aligned with industry advancements.