Intern – Machine Learning Security (Applied ML & Fault Injection)
About The Position
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. Help us make fault injections smarter. You will also focus on enhancing fault injection techniques across multiple domains, including voltage, clock, electromagnetic, and laser methods using learning algorithms. You’ll design and run adaptive, data‑driven experiments that automatically tune fault‑injection parameters (type, timing, intensity, frequency) using learned models—moving beyond static, domain‑tuned heuristics to algorithms that learn and improve as new data arrives. You’ll work hands‑on with instrumentation/test workflows and our AI/ML platform to build a closed‑loop optimization pipeline that raises failure coverage, shortens test time, and increases reproducibility.
Requirements
- Currently pursuing a MS/PhD in EE/CE/CS or related field.
- Solid Python and ML fundamentals (supervised/unsupervised learning, overfitting, uncertainty).
- Experience with one optimization method (Bayesian optimization, bandits, RL) or hyperparameter tuning at scale.
- Data handling/visualization (NumPy/Pandas/Matplotlib), version control (Git).
- Candidates who wish to be considered must be enrolled in a accredited college/university as of September 2026. Applicants who have graduated before September 2026 will not be considered unless they are entering/applying to a MS or PHD program after graduating.
- Visa Sponsorship is not available for this position. Candidates who now or at any point in the future require sponsorship for employment visa status (e.g., H-1B Visa status) may not be considered.
Responsibilities
- Model‑guided parameter optimization: Build and evaluate optimization loops (Bayesian optimization, bandits, RL) that select fault‑injection parameters to maximize coverage or detection sensitivity under resource constraints.
- Experiment design & telemetry: Define Design of Experiments/sequential experiment plans, log metrics/parameters/artifacts, and instrument robust telemetry for analysis and replay.
- Adaptive learning: Implement feedback loops so models update with every run, improving the next round of injections (active learning).
- Scalable tuning: Use distributed hyperparameter search to explore large parameter spaces efficiently.
- Reliability metrics: Define and track objective functions (e.g., fault detectability, coverage, time‑to‑fail, false positives/negatives), plus safety/guardrails for destructive tests.
- Impact demo: Deliver a working prototype that can be run by engineers via our platform (scripts + config + dashboards) and present measurable improvements vs. baseline domain‑tuned flows.
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What This Job Offers
Career Level
Intern
Number of Employees
5,001-10,000 employees
