Okay, I've searched for Embedded Linux Developer Engineer roles in the UK or Ireland within the technology industry, for remote positions, requiring 7+ years of experience and proficiency in C/C++. I have found several positions for you.

Sure. Here's the analysis:

Job Analysis:

This role is fundamentally about advancing embedded machine learning capabilities on edge devices, integrating AI-driven solutions directly into hardware with constrained resources. The Embedded Linux Dev Engineer will be pivotal in expanding the range and performance of embedded devices capable of running Edge Impulse’s ML frameworks, enabling real-time, local decision-making across diverse sensor inputs like image, audio, and motion. Success in this role requires someone with substantial embedded software expertise—particularly in C/C++—and an understanding of embedded Linux systems, peripheral control, and system-level debugging. The job demands both deep technical proficiency and creative problem-solving to develop novel algorithms and improve tooling that bridges embedded devices with larger ecosystems. Collaboration across distributed teams is important, but so is demonstrating ownership and the initiative to drive solutions independently. Challenges likely include optimizing ML workloads for resource-constrained environments, integrating heterogeneous sensors, and navigating the complexities of embedded Linux and real-time requirements. Early success would be measured by delivering reliable, scalable embedded ML deployments, improving system integrations, and enhancing the efficiency and capability of Edge Impulse’s embedded infrastructure.

Company Analysis:

Qualcomm Technologies International Ltd operates at the forefront of mobile, industrial IoT, and edge AI innovation, blending startup agility through its partnership with Edge Impulse and the robust backing of a global tech leader. This positions the company as both a pioneering disruptor and a stable industry player, focusing on transforming how machine learning is deployed on edge devices worldwide. The culture is likely dynamic and innovation-driven, valuing technical excellence and ownership mindset, balanced with teamwork across distributed engineering groups. For a candidate, this means a stimulating environment where creative problem-solving and ownership are rewarded, with exposure to cutting-edge AI applications integrated into real-world embedded systems. The role fits within a broader strategy to diversify Qualcomm’s portfolio by deeply embedding ML in edge hardware, supporting not only growth but also shaping industry standards. Given the strategic importance of edge AI, this position offers visibility and influence, making it ideal for engineers eager to impact the next generation of intelligent devices while benefiting from Qualcomm’s extensive resources and market reach.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time you had to deliver an embedded software solution under tight resource constraints. How did you ensure both performance and reliability?
• Behavioral: Describe a situation where you worked remotely or in a distributed team. How did you maintain alignment and drive progress independently?
• Behavioral: Can you share an experience where you overcame a challenging debugging or integration issue with embedded Linux or related toolchains?
• Behavioral: Give an example of when you took ownership of a project from concept to delivery. What was your approach and outcome?
• Technical: How do you approach optimizing machine learning algorithms for deployment on resource-constrained embedded devices?
• Technical: Explain your experience working with embedded Linux distributions like Buildroot or Yocto and how you have customized them for specific hardware targets.
• Technical: What techniques and tools do you use to debug complex embedded systems, particularly when dealing with real-time sensor data pipelines?
• Technical: Describe your experience developing or integrating computer vision applications for embedded platforms, particularly using OpenCV or GStreamer.
• Situational: Imagine you discover that an embedded device is dropping sensor data intermittently, causing inconsistent ML inference results. How would you diagnose and address this problem?
• Situational: If you had to design a new feature that connects a novel sensor type to Edge Impulse’s platform, what key considerations would guide your software architecture and development approach?
• Situational: Suppose there's a conflict between priorities from cross-functional teams—hardware engineers wanting faster time-to-market and data scientists needing more computational headroom for ML models. How do you navigate this?
• Company Fit: Why are you interested in working with Qualcomm and Edge Impulse specifically, given their combined focus on edge AI and embedded systems?
• Company Fit: How do you feel your personal values or professional goals align with Qualcomm’s mission to deliver intelligent computing everywhere?
• Company Fit: What attracts you to the challenges of delivering embedded machine learning solutions on edge devices compared to traditional cloud-based AI?
• Candidate Questions: Can you describe what a typical project cycle looks like for this role and how cross-team collaboration is managed?
• Candidate Questions: How does Qualcomm support ongoing learning and professional development for embedded software engineers working on edge AI?
• Candidate Questions: What are the biggest technical challenges the team is currently facing in integrating machine learning with embedded Linux at Qualcomm?