Principal Engineer/Architect - Generative AI & Edge Systems (Automotive)

Position: Principal Engineer/Architect – Generative AI & Edge Systems, Automotive

Location: San Jose CA (4 days onsite)

Employment Type: Full-Time

Overview

We are partnering with a top-five semiconductor company to hire Principal Engineer / Architect - Generative AI & Edge Systems (Automotive).

This is an exciting opportunity to join a world-class automotive engineering team developing next-generation Generative AI and deep learning solutions for edge computing platforms. Working across silicon, software, and automotive customer teams, you will help bring cutting-edge AI technologies into production for future in-vehicle systems.

Key Responsibilities

• Research and develop Generative AI and deep learning-based systems for in-vehicle domains - Infotainment, Speech, UI, and multimodal ADAS - with a focus on architectures deployable on resource-constrained edge hardware.
• Lead integration of GenAI frameworks and toolchains optimized for automotive edge compute, driving adoption of cutting-edge on-device AI techniques from research into production.
• Partner with Silicon and Software engineering teams and automotive customers to define and deliver technical solutions for GenAI and LLM requirements, with edge feasibility as a core design constraint from the start.
• Own end-to-end LLM optimization for on-device deployment on GPUs and NPUs - profiling models, identifying bottlenecks, and driving improvements at both the model level (quantization, distillation, pruning) and framework level to maximize inference performance within embedded compute and power budgets.
• Track industry and academic advances in edge AI and efficient LLM techniques, translating relevant breakthroughs into practical, deployable improvements for in-vehicle systems.

Required Skills & Experience

• Master's degree in Computer Science, Electrical Engineering, Mathematics, or related field, with 10+ years of overall software engineering experience, including 4+ years directly relevant to GenAI/LLM systems.
• Deep hands-on experience with ML/DL frameworks and tooling: TensorFlow, PyTorch, NeMo, TAO, TensorRT, CUDA, and vLLM, with working knowledge of edge-inference frameworks (e.g., TensorRT-LLM, ONNX Runtime, GGUF/llama.cpp).

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• Proven experience optimizing state-of-the-art models for edge deployment - quantization, pruning, distillation - with practical tradeoff analysis across latency, memory bandwidth, and CPU vs. NPU vs. GPU compute.
• Hands-on experience deploying and optimizing multiple LLMs and multimodal models concurrently on edge devices - managing shared compute/memory budgets, model orchestration, and runtime tradeoffs when running several models side-by-side on constrained hardware.
• Practical experience with speech and multimodal AI pipelines - ASR, TTS, speech-to-intent, or vision-language fusion - in resource-constrained, real-time environments.
• Strong programming proficiency in C, C++, and Python, including performance-critical and memory-constrained code.
• Hands-on experience with embedded software, RTOS, and microcontroller-based platforms.
• Familiarity with training data preparation, curation, and open-source datasets for fine-tuning or evaluation.
• Working knowledge of in-vehicle AI technical stacks - Speech, Voice, or ADAS/AD systems.
• Exposure to automotive functional safety (ISO 26262) and cybersecurity (ISO 21434) standards.
• Demonstrated ability to move quickly in agile environments, translating research and prototypes into shippable, production-grade systems.

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Additional Information

• This is a hands-on Individual Contributor role at the Principal Engineer / Architect level.
• Candidates should be willing to work onsite in San Jose, CA.
• Relocation support is available for this role

INTERESTED?

We are committed to submitting suitable candidates for this vacancy to our client ASAP, for more information, contact Renz Moreno at PER Recruitment or send your CV to [email protected]