Intern - Machine Learning Security (Applied ML & Fault Injection)

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, datadriven experiments that automatically tune faultinjection parameters (type, timing, intensity, frequency) using learned models-moving beyond static, domaintuned heuristics to algorithms that learn and improve as new data arrives. You'll work handson with instrumentation/test workflows and our AI/ML platform to build a closedloop optimization pipeline that raises failure coverage, shortens test time, and increases reproducibility.

1. Modelguided parameter optimization: Build and evaluate optimization loops (Bayesian optimization, bandits, RL) that select faultinjection parameters to maximize coverage or detection sensitivity under resource constraints.
2. Experiment design & telemetry: Define Design of Experiments/sequential experiment plans, log metrics/parameters/artifacts, and instrument robust telemetry for analysis and replay.
3. Adaptive learning: Implement feedback loops so models update with every run, improving the next round of injections (active learning).
4. Scalable tuning: Use distributed hyperparameter search to explore large parameter spaces efficiently.
5. Reliability metrics: Define and track objective functions (e.g., fault detectability, coverage, timetofail, false positives/negatives), plus safety/guardrails for destructive tests.
6. Impact demo: Deliver a working prototype that can be run by engineers via our platform (scripts + config + dashboards) and present measurable improvements vs. baseline domaintuned flows.

• 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.

Keysight Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.

The level of role will be based on applicable experience, education and skills; Most offers will be between the minimum and the midpoint of the Salary Range listed below.

California pay range: MIN $60.82-$65.50/hour

Note: For other locations, pay ranges will vary by region.

Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***