Senior Machine Learning Engineer – LLMs & Agentic AI
Overview:
Keysight is on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our \~15,000 employees create world\-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.
Our award\-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry\-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers. About Keysight AI Labs
Keysight’s AI Labs is a global R\&D group pioneering the integration of machine learning, generative AIinto Keysight’s test, measurement, and design solutions. Our mission is to transform how engineers design, simulate, and validate advanced systems\- from 6G and semiconductors to quantum and automotive \- by embedding AI throughout our workflows. About the AI TeamJoin *Keysight's central AI Hub in the heart of Barcelona.* We are expanding our newly formed AI Team.As part of this growing team, you will join a vibrant, cross\-functional environment that brings together experts in ML engineering, data science, physics\-informed modeling, and software development. You’ll work closely with domain experts across RF, EM, circuit design, and test \& measurement to accelerate scientific innovation through AI. About the Role
We are looking for a Machine Learning Engineer (senior level preferred) to develop and productize advanced LLM\-based, agentic, and generative AI pipelines.
You’ll design scalable architectures that integrate AI into Keysight’s software and hardware platforms, enabling intelligent workflows, root\-cause analysis, automated scripting, anomaly detection, and adaptive decision\-making.
This role blends research, engineering, and applied productization, ideal for those who enjoy turning cutting\-edge ML concepts into deployable real\-world solutions.
Responsibilities:
- Collaborate with Keysight domain experts (RF, 6G\-wireless, EM, circuit, and measurement) to gather requirements, physical constraints, and workflow insights for ML pipeline design.
- Design and implement SOTA ML architectures including LLMs, agentic systems, GANs, diffusion models, and RAG pipelines for data augmentation, anomaly detection, modeling, and automation.
- Develop scalable ML pipelines for on\-device, on\-prem, cloud, and hybrid GPU environments, ensuring efficiency, reliability, and scalability.
- Write production\-grade Python, C\+\+, and CUDA code following best practices (testing, CI/CD, documentation, performance profiling).
- Collaborate with product teams to integrate ML\-driven features into Keysight’s commercial products.
- Continuously explore and apply new research in LLMs, agentic reasoning, multimodal AI, and generative architectures to enhance Keysight’s capabilities.
Required Qualifications* Education: Master’s or PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
- Strong ML/DL foundations: solid understanding of neural architectures, optimization, and evaluation metrics.
- Hands\-on experience with PyTorch (preferred) or TensorFlow.
- Proven expertise building or fine\-tuning transformer architectures (GPT, T5, LLaMA, etc.).
- Experience with LLM fine\-tuning, instruction tuning, RLHF, PPO/DPO, or similar adaptation techniques.
- Strong coding skills in Python and familiarity with CI/CD, testing, Git versioning, and containerization (Docker/Kubernetes).
- Experience with data pipelines (tokenization, preprocessing, large text corpora).
- Experience with MLOps tools (MLflow, Weights \& Biases, Ray).
- Experience with agentic workflows, RAG systems, or multimodal (text, code, signal) applications.
- Excellent communication and teamwork skills; comfortable working in cross\-functional R\&D environments.
- Experience optimizing models for edge or embedded environments.
- Knowledge of model compression, quantization, or inference optimization.
- Research literacy and the ability to read, reproduce, and extend SOTA papers.
- Open\-source contributions or public ML repositories are a strong plus.
- Prior experience with Keysight software, test and measurement workflows, or domain\-specific modeling is highly valued.
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