Senior Embedded AI Engineer

Location: Ramat Hahayal, Tel Aviv

Employment Type: Full-time

Company: GSI Technology – A publicly traded, international high-tech company (NASDAQ: GSIT) developing the cutting-edge Gemini® Associative Processing Unit (APU) for computer-in-memory acceleration.

Position Overview

We are seeking a highly skilled and motivated Senior Embedded AI Engineer to lead the development and optimization of AI models — including Large Language Models (LLMs) and Large Vision Models (LVMs) — on GSI’s proprietary APU. This role bridges high-level machine learning understanding with low-level system and performance engineering, primarily in Python ,C and C++. You will be responsible for architecting, implementing, and optimizing AI pipelines under hardware constraints, with a strong emphasis on computer vision and transformer architectures.

Key Responsibilities

• Develop and optimize software libraries for LLM and LVM implementations on embedded hardware.
• Design end-to-end system flows integrating AI models, especially in computer vision domains.
• Lead performance tuning efforts under constraints such as memory, compute, and latency.
• Work closely with hardware teams to co-design software optimized for GSI’s APU.
• Debug and optimize AI inference pipelines, including Python-based pre/post-processing where applicable.

Required Qualifications

• B.Sc. in Computer Science or Electrical Engineering from a leading university.
• 5+ years of experience in embedded software development using C++ and C.
• Solid experience in one or more of the following: Computer Vision, RT-Embedded, DSP.
• Proven experience in developing and optimizing AI pipelines under performance, memory, and latency constraints.
• Proven track record in performance/memory-constrained programming.
• Strong communication skills, analytical mindset, and attention to detail.
• Independent, solution-oriented, and highly motivated to make things happen.

Preferred Qualifications

• Practical experience with transformer architectures and/or vision-language models (VLMs).
• Deep knowledge of computer vision pipelines and multimodal systems.
• Experience designing complex software systems from concept to deployment.
• Familiarity with hardware-aware optimization techniques such as:
• Quantization
• Pruning
• Kernel fusion
• Experience with performance profiling tools (e.g., PyTorch Profiler, NVIDIA Nsight).
• Low-level optimization experience with CUDA, OpenCL, or hardware-specific SDKs.

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