Role Overview:

Provide technical and managerial leadership for the EDGE team across two core pillars: leading a low-level software development team building a unified platform for AI model execution on edge devices, and managing the EDGE laboratory (hardware assets, testing, and quality validation). This role bridges system architecture, advanced optimization, and the establishment of structured pipelines from POC to final deployment.

Key Responsibilities:

Development Team Leadership: Manage and mentor C/C++ low-level engineers, define platform architecture, and build a modular framework that abstracts hardware differences for diverse AI deployments.
System & Low-Level Development: Oversee development close to hardware (drivers, OS, runtimes), manage system resources (memory, multithreading), and drive profiling and benchmarking in resource-constrained, low-latency environments.
EDGE Lab Management: Take full ownership of laboratory infrastructure, monitor workspace readiness, allocate devices to active projects, and integrate new hardware components (e.g., NVIDIA Jetson, SIMA).
Quality Assurance & Gatekeeping: Establish strict technical standards, perform rigorous system validation (testing latency, memory, and power efficiency), and provide final technical sign-off before deployment.
Process & Methodology Drive: Standardize and scale workflow methodologies to ensure a repeatable pipeline from early stage proof-of-concept to production (Development → Testing → Optimization → Deployment).

Leadership Experience: Proven track record in team management or strong technical leadership (Tech Lead) managing complex engineering environments. Development Background: 10+ years of system-level development experience, with a deep focus on Low-Level, Embedded, or System Software. Languages & OS: Expert-level proficiency in C and C++, alongside extensive system-level Linux experience. Architecture & Performance: Deep knowledge of computer architecture, memory management, and concurrency, with proven expertise in system-level debugging, profiling, and performance tuning. Hardware Integration: Practical hands-on experience working directly with EDGE hardware and hardware-software interfaces. Nice-to-Have Requirements: Hands-on familiarity with NVIDIA Jetson and SIMA platforms. Prior experience managing/establishing hardware labs, benchmarking environments, or validation processes. Background in AI/ML inference at the Edge.