Define and lead the ML model roadmap, progressing from binary malware/benign classification through multi-class threat taxonomy to behavioral attack-pattern detection.

Architect training pipelines that scale to a growing malware variant library with reproducible, versioned experiments; establish model quality gates for production promotion.

Lead research into advanced detection techniques including behavioral sequence modeling and detection of novel, previously unseen threat categories.

Optimize multi-class ML classifiers for NPU inference against throughput and latency requirements; collaborate with hardware teams on NPU capability requirements.

Drive dataset strategy including coverage across threat categories, synthetic data generation and dataset quality standards.

Mentor MTS ML engineers; lead model and code reviews; establish best practices for reproducibility, documentation and experimental rigor. Represent ML model strategy in architecture reviews, external partner technical meetings and potential research publications.

7+ years of applied ML experience, including 3+ years in security, anomaly detection or hardware/systems ML.

Expert-level proficiency in PyTorch or TensorFlow; strong Python; experience leading ML platform or infrastructure decisions. Demonstrated record of taking ML models from research to production deployment.

Experience with model deployment on GPU, NPU or other specialized hardware accelerators.

Ability to lead technical direction and influence cross-functional teams.

Research or industry experience in hardware-assisted security, side-channel analysis or microarchitectural security.

Familiarity with AMD compute toolchains or AMD NPU inference frameworks.

Knowledge of attack classification frameworks and enterprise threat hunting methodologies.

Experience collaborating with endpoint security software vendors.