Senior Research Engineer in Artificial Intelligence and Embedded Systems

Senior Research Engineer – AI & Embedded Systems

Position Overview

A research-driven technology organization is seeking a Senior Research Engineer specializing in Artificial Intelligence and Embedded Systems.

This role focuses on bridging advanced AI research with system-level implementation in resource-constrained environments. The position requires strong foundations in AI combined with low-level system software expertise, enabling the development of efficient, high-performance solutions for embedded platforms.

Key Responsibilities

AI System Architecture & Innovation

• Analyze business and system requirements to design AI-driven architectures that address performance gaps and future scalability needs.
• Propose innovative solutions to enhance system competitiveness and efficiency.
• Evaluate trade-offs between performance, resource usage, and system constraints in embedded environments.

Research & Technology Scouting

• Monitor advancements in AI, embedded systems, and system software.
• Translate academic and industry innovations into practical solutions and internal technology strategies.
• Contribute to long-term technical direction and product competitiveness.

Embedded System Development

• Contribute to the development of software subsystems for embedded and network-oriented devices.
• Work across system layers, including:
  • Drivers
  • Operating systems
  • System libraries
• Optimize AI workloads for constrained environments (compute, memory, power).

Required Qualifications

• PhD in Artificial Intelligence or a closely related field, with strong foundational expertise in AI (core training during doctoral studies).
• Solid background in system software, including:
  • Operating systems
  • Low-level programming
  • Drivers and system libraries
• Proficiency in programming languages such as:
  • C / C++
  • Python
  • Rust (or similar systems programming languages)

Preferred Qualifications

• Experience working with embedded systems, particularly in environments with strict resource constraints.
• Background in AI for embedded or edge devices.
• Experience with networked or communication-oriented embedded systems.
• Familiarity with performance optimization in constrained hardware environments.

Ideal Candidate Profile

• Research experience focused on AI applied to embedded or edge systems.
• Ability to bridge theoretical AI models with efficient real-world implementations.
• Strong understanding of both high-level AI algorithms and low-level system behavior.
• Experience addressing performance and scalability challenges in constrained environments.

Personal Attributes

• Strong analytical and problem-solving mindset.
• Ability to work independently while collaborating across multidisciplinary teams.
• Curiosity and proactive approach to emerging technologies.
• Clear communication skills for both technical and non-technical stakeholders.
• Structured and solution-oriented working style.