R&D Engineer 5, Software

Overview

Keysight accelerates innovation to connect and secure the world. Our solutions span wireless communications, semiconductors, aerospace / defense, automotive, and beyond. We combine measurement science, simulation, and advanced AI to help engineers design, simulate, and validate the world's most advanced systems. The Keysight AI Labs is pioneering scientific machine learning and physics & T&M-informed AI to transform Keysight's software, simulation, and measurement products. We are looking for a versatile Software Developer who can operate both horizontally across multiple services. You'll play a key role in designing, building, and scaling services while contributing to infrastructure, backend, and frontend needs. The ideal candidate is flexible, thrives in dynamic environments, and can deliver high-quality solutions quickly.

Keysight is at 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.

Responsibilities

Key Responsibilities

• Design and implement MLOps pipelines for training, validation, deployment, and monitoring of machine learning models.
• Develop and maintain infrastructure for data versioning, model registries, and experiment tracking (e.g., MLflow, LakeFS, Airflow).
• Integrate orchestration tools (e.g., Kubeflow, Ray, Airflow) to support automated workflows and distributed training.
• Collaborate with data scientists and software engineers to ensure seamless model handoff and deployment.
• Build APIs and SDKs to abstract infrastructure complexity and enable self-service model development.
• Implement monitoring and alerting systems for model drift, performance degradation, and system health.
• Support on-prem and cloud-based deployments (e.g., Kubernetes, HPC clusters, AWS).

Qualifications

Required Qualifications

Preferred Qualifications

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Skills Required

Airflow, Vue, Typescript, React, Aws, Node.js, FastAPI, Kubernetes, Python, Docker, Flask, Javascript