ML Engineer, Model Training - Senior Member of Technical Staff (SMTS)
⚲ Gdansk
Do uzgodnienia
Wymagania
- TensorFlow
- Python
Opis stanowiska
AMD is building a hardware-assisted security platform that uses silicon-level Performance Monitoring Counters (PMCs) and on-chip machine learning to detect advanced endpoint threats (ransomware, fileless malware, cryptojacking) at the processor layer, below OS-based evasion. The platform collects CPU behavioral telemetry, classifies it via an ML inference engine, and exposes threat signals to security-software partners through a standardized API. The team covers the full stack: silicon telemetry, ML training/validation, real-time inference, lab qualification and CI/CD.
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.
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.
🔍 Dekoder Ogłoszenia
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Define and lead the ML model roadmap
Oczekuje się, że będziesz samodzielnie tworzyć i kierować strategią rozwoju modeli uczenia maszynowego, a nie tylko realizować istniejące plany.
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progressing from binary malware/benign classification through multi-class threat taxonomy to behavioral attack-pattern detection
Zadania mogą obejmować szeroki zakres złożoności, od prostych klasyfikacji po bardzo zaawansowane techniki wykrywania.
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Architect training pipelines that scale to a growing malware variant library with reproducible, versioned experiments
Będziesz musiał zaprojektować systemy, które poradzą sobie z dużą ilością danych i złożonymi eksperymentami, co może wymagać znacznego nakładu pracy.
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Optimize multi-class ML classifiers for NPU inference against throughput and latency requirements
Praca będzie wymagała optymalizacji modeli pod kątem specyficznego sprzętu (NPU), co może być wyzwaniem technicznym.
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Mentor MTS ML engineers
Oprócz pracy technicznej, będziesz odpowiedzialny za rozwój i wsparcie mniej doświadczonych członków zespołu.