Senior Machine Learning Engineer – LLMs & Agentic AI

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 AI

into 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 Team Join

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

Senior Machine Learning Engineer

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.

Desired Qualifications Familiarity with

cloud environments

(Azure, AWS, or GCP). 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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***Keysight is an Equal Opportunity Employer.**