Job Description
Overview of the role:
Keysight AI Labs is looking for PhD students currently pursuing Machine Learning or Computer-Vision-related studies to join our AI R&D team in Barcelona for a 6-month R&D internship. This position is open to students across seniority levels, with preference to experienced, PhD candidates. If selected, you will contribute to the development of advanced ML systems supporting strategic Keysight initiatives using various types of state-of-art (SOTA) and novel ML/DL models and other advanced ML pipelines for different problems and products. The role combines research, engineering, and productization of ML technologies in a collaborative and fast-paced environment. Therefore, domain specific knowledge and experience with Keysight's tools and business will be preferred.
Responsibilities
- Collaborate with Keysight engineering experts (RF, 6G-wireless, EM, circuit, measurement, etc.) and other ML engineers to design, develop and deploy ML pipelines for both proof-of-concept and production environments, including on-device, hybrid, edge and on-cloud infra.
- Design state-of-art (SOTA) ML-based systems to detect and classify different types of signals, images, videos, etc.
- Perform image segmentation, bounding box and other relevant techniques for such multi-domain and dynamic data in real time.
- Detect and classify anomalies within different types of data such as signals, images, videos, etc.
- Contribute to synthetic data generation and augmentation pipelines to improve model robustness.
- Develop and optimize SOTA deep learning and foundational models (e.g., CNNs, Vision Transformers, etc.) for classification, clustering, and anomaly detection.
- Write good quality code in Python, C++, and CUDA following best coding practices.
- Apply CI/CD practices, code testing, documentation, and performance profiling.
- Work with product teams to integrate ML/AI-driven pipelines and tools into Keysight’s commercial platforms.
- Stay ahead of SOTA ML model research, bringing new methods into Keysight workflows.
- Support the integration of developed models into existing or new product platforms, ensuring performance and real-world constraints.
- Participate in cross-functional meetings and tech talks to share insights and demo prototypes.
- Contribute to the R&D Keysight AI Labs internal and external knowledge sharing efforts via publications, invention disclosures, blog posts, etc.
- Publish findings and contribute to open-source tools and frameworks where applicable.
Qualifications
- Pursuing a PhD in Applied Mathematics, Scientific Computing, Computer Science, Electrical Engineering, Telecommunications, or related discipline.
- Publications in top ML conferences (NeurIPS, KDD, ICML, ICLR, etc.) and other system conferences and journals.
- Strong ML/DL Foundations: Deep understanding of neural networks, statistics, optimization, and model evaluation metrics.
- Proficiency in ML Frameworks: Strong skills in PyTorch (preferred), TensorFlow, Scikit-learn.
- Strong experience in deep learning architectures such as CNNs, vision transformers, variational autoencoders, and attention-based models, especially for image classification, segmentation, object detection and classification, anomaly detection, image reconstruction, etc.
- Hands-on experience of deploying such models in production environments, including optimization for real-time inference and edge devices is a plus.
- Experience with performance optimization, knowledge of model compression, quantization, and inference optimization techniques is a plus.
- Experience with MLflow, Weights & Biases, etc., is a plus.
- Familiarity with cloud platforms (Azure, AWS, GCP) and containerization (Docker, Kubernetes) is a plus.
- Experience with writing production-quality python code, testing, CI/CD, and version control (Git) is a plus.
- Understanding of wireless protocol layers (e.g., RRC, NAS, PHY) and ability to engineer domain-specific features from network logs is a big plus.
- Ability to propose and evaluate novel ML architectures and solutions under ambiguity.
- Strong communication skills and ability to articulate complex ideas clearly in English.
- Interest in team culture and collaborative problem-solving.
- Cross-Functional Collaboration: Ability to communicate and collaborate with researchers, engineers, and product teams.
- Research Literacy: Ability to read, reproduce, and extend recent ML research papers; open-source contributions are a plus.
