Keysight is looking to solve the problem of developing a next-generation agentic orchestration framework that enables AI agents to reason, adapt, and coordinate across complex engineering workflows, pushing past human limits in design space exploration, optimization, and evolution of orchestration strategies.
Requirements
- Strong foundation in computer science fundamentals (data structures, algorithms, and distributed systems) and their application to ML systems
- Proven experience developing neural or hybrid ML models for engineering, physics, or signal-processing domains
- Hands-on experience with data preprocessing, feature engineering, and pipeline automation (Python, SQL, or equivalent)
- Proficiency in PyTorch, libtorch, or similar frameworks for model development and training
- Experience implementing XAI methods for scientific or engineering models
- Background in scientific computing, simulation-driven modeling, or surrogate model development
- Familiarity with hybrid physical-statistical modeling techniques
Responsibilities
- Build the model intelligence and feedback infrastructure that allows engineering models to generalize across varying design and measurement scenarios
- Develop predictive and surrogate models using experimental, simulation, and sensor data
- Design feature representations and conditioning schemas that encode physical parameters, system constraints, and test configurations
- Implement model pipelines capable of adapting to new devices, topologies, or domains with minimal retraining
- Develop data ingestion, transformation, and validation pipelines for structured, semi-structured, and streaming data
- Implement feedback loops where new simulation and measurement results automatically trigger data updates and retraining
- Integrate Explainable AI (XAI) methods into model training and validation workflows
Other
- PhD or 5+ years of experience in machine learning, applied data science, computational modeling, or related technical fields
- Strong programming proficiency in Python, with experience in C++ integration for high-performance model components
- Experience using data management and analytics tools (e.g., pandas, NumPy, Apache Arrow, SQL)
- Familiarity with experiment tracking and MLOps tools (e.g., MLflow, DVC, or equivalent)
- Demonstrated ability to apply statistical analysis, uncertainty modeling, and visualization to engineering datasets
