ML Engineer, Model Training - Senior Member of Technical Staff (SMTS)

Description

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.

What we offer

• Global Relocation - (Relocation options; Experience in an international environment; Cross-cultural experience)

• Recognition and Evaluation - (Feedback culture; Regular appraisals)

• Time Off - (Annual holiday - 20 or 26 days. The duration of the leave depends on the overall seniority; Occasional leave - 1 or 2 days/ depending on the circumstances; Child care leave - 2 days or 16 hours per year; Absence due to force majeure - 2 days or 16 hours per year; Maternity Leave - 20 weeks; Parental Leave - 41 weeks; Paternity Leave - 14 days)

• Luxoft Training Center - (Expert-led tech courses covering basic to advanced topics; Internal instructor-led soft skills courses; Comprehensive in-house self-learning resources for both soft and hard skills; Access to external self-learning libraries like ProQuest eBook and Udemy for Business; Cloud Programs: MS Cloud Academy, AWS Partner Academy, Google Cloud Academy; Custom Learning Programs: upskilling, reskilling, technical mentorship; Leadership Programs for Managers)

• Well-being and Work-life Balance - (Multisport card; Possibility to order Multisport card at the corporate rate for family members; LuxGood Program: wellbeing seminars, contests, relaxation sessions, yoga sessions, etc.; One Team Program: Buddy for each New Joiner; seminars, meeting and workplace space to support integration with local community and culture; “Hire me” workshops for partners; Preferential banking offer; Preferential car leasing offer; Cafeteria program discounts for shops, cinema tickets, holiday offers; Luxoft Social Benefit Fund: sport and recreation benefits, the possibility to receive financial support)

• Health Care - (Private Healthcare Insurance with unlimited access to specialists; Full dental support; Travel Insurance; Possibility to add private healthcare coverage for family members at the corporate rate; Life insurance at the corporate rate for employees and family members, including payment of the basic package for the employee by the employer; Reimbursement for corrective glasses)

• Company Events and Friendly Environment - (Many fun social activities organized by the Luxoft team offline in your city; Online entertainment events for whole company and local team events; A workplace where you’re treated with respect within a multicultural team)

• Internal Mobility - (Rotation between projects and accounts; New career opportunities)

• Self-Learning Library

• CSR Projects

Other

• Languages: English: B2 Upper Intermediate

• Seniority: Senior

Requirements

• 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.

Responsibilities

• 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.