Agentic & Generative Edge AI Optimization Engineer

Agentic & Generative Edge AI Optimization Engineer

What You’ll Do

• Optimize LLMs and multimodal models for on-device deployment
• Investigate, develop and apply advanced quantization (8-bit, 4-bit, mixed precision), pruning, and distillation techniques for deriving optimized models for NPU targets.
• Accelerate inference performance
• Investigate, develop and implement system optimizations such as speculative decoding and other efficient decoding algorithms tailored for edge environments.
• Engineer agentic AI capabilities towards tiny agents
• Investigate methodologies for enhancing the performance of small language models towards enabling tiny agents at the edge, while ensuring these follow safety principles.
• Work with inference engines and deployment frameworks
• Deploy optimized models using Ollama, llama.cpp, ONNX Runtime, and TFLite for efficient NPU inference.
• Benchmark LLMs and agentic systems
• Design benchmarking pipelines for assessing the performance of Generative and Agentic AI systems on-device.
• Develop demonstrators and proof-of-concepts
• Build technology PoCs for NXP relevant use-cases such as industrial safety monitoring, in-cabin sensing, and other edge AI applications for showcasing key technologies.
• Move key technologies from research into product solutions
• Translate advanced optimization techniques and agentic AI features into production-ready implementations and collaborate with product teams to integrate these features into SW/HW portfolio.

Your Profile

• MSc, PhD or EngD in a technical specialism, like Computer Science or equally relevant.
• 5+ years of experience in software/AI engineering with deep exposure to LLMs, VLMs, and systems performance.
• Experience with LLM quantization techniques (e.g., SmoothQuant, SpinQuant, QuaRoT), pruning (Wanda, SparseGPT, etc.) and other system optimizations like speculative decoding.
• Track-record experience in working with AI frameworks (PyTorch, TensorFlow, etc.), required.
• Experience with Agentic AI technologies and familiarity with existing frameworks (e.g., LangChain, Google ADK, SmolAgents, etc.)
• Understanding of safety and security considerations for agentic systems (e.g., guardrails, policy enforcement, secure function calling) is a plus.
• Understanding of AI toolchains, deployment, portability and inference engines (CUDA, TensorRT, TFLite, ONNX, Ollama, etc.) preferred.
• Affinity and experience with embedded systems, and NPU accelerators required.
• Experience with embedded software architecture, build systems, version control systems required.
• Broad experience with Operating systems GNU/Linux, embedded systems, development boards, and processors, and SW competencies required.
• Familiarity with setting up and maintaining related ML-Ops development environments (MLFlow, ClearML, etc.) required.
• Knowledge of build systems (YOCTO, OpenEmbedded, etc.) beneficial, working with cross-compilation toolchains for ARM preferred.
• Solid programming experience of C, C++, Python and Bash programming languages on Linux systems required.