Edge AI & SDR Firmware Intern

Rohde & Schwarz (China) Technology Co., Ltd.; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

Rohde & Schwarz (China) Technology Co., Ltd. seeks researchers to map the edge AI hardware landscape, evaluate SDR-related ecosystems, and analyze workloads for SDR functions. The role involves building proofs-of-concept and collecting performance data to document findings.

The candidate will explore hardware platforms and software toolchains, documenting results and recommendations for SDR deployments across the edge AI stack.

Qualifications

• Experience in edge AI hardware research and documentation.
• Familiarity with SDR (Software Defined Radio) concepts and related workflows.
• Ability to analyze hardware ecosystems and evaluate software toolchains.

Responsibilities

• State-of-the-Art Research: Research and document the current landscape of edge AI hardware, including processors, accelerators, and related technologies (e.g., GPUs, NPUs,).
• Ecosystem Exploration: Investigate the software ecosystems surrounding identified hardware platforms (e.g. NVIDIA Aerial), including development tools and community resources, with a focus on SDR compatibility.
• SDR Workload Analysis: Assessment of how different edge AI hardware options could be applied to key Software Defined Radio functions.
• Proof-of-Concept: Develop and test simple proof-of-concept implementations of SDR functionality on selected hardware.
• Performance Data Collection: Gather and organize performance data from hardware evaluations.
• Documentation & Reporting: Creation of documentation summarizing research findings and experimental results.

Job description

Rohde & Schwarz (China) Technology Co., Ltd. seeks researchers to map the edge AI hardware landscape, evaluate SDR-related ecosystems, and analyze workloads for SDR functions. The role involves building proofs-of-concept and collecting performance data to document findings.

The candidate will explore hardware platforms and software toolchains, documenting results and recommendations for SDR deployments across the edge AI stack.