ML Engineer

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: Regular

Requirements

• 3+ years of industry experience in applied ML or data science.

• Proficiency in Python; strong hands-on experience with PyTorch, TensorFlow or scikit-learn.

• Experience with binary or multi-class classification on tabular or time-series data. Solid understanding of model evaluation: cross-validation, precision/recall, F1, ROC-AUC.

• Understanding of model optimization for inference: quantization, pruning, ONNX export.

• Experience with anomaly detection or one-class classification methods. Background in cybersecurity, malware analysis or endpoint threat detection.

• Familiarity with hardware performance counters or systems-level telemetry as ML input features.

• Experience training models for deployment on GPU or NPU accelerators with constrained compute budgets.

Responsibilities

• Design, train and evaluate ML classifiers (binary and multi-class) on CPU PMC telemetry datasets targeting ransomware, cryptomining, fileless malware and related threat categories.

• Perform feature engineering on raw hardware performance counter data (branch behavior, cache miss patterns, instruction mix ratios, execution port utilization) to extract discriminative threat signatures.

• Implement evaluation frameworks measuring detection rate, false-positive rate and inference latency on target GPU/NPU hardware.

• Expand and validate training datasets across malware variants; iteratively improve model coverage and accuracy.

• Optimize model architectures for inference on AMD integrated GPU and NPU hardware, balancing accuracy against strict CPU overhead targets. Export models to production-compatible inference formats and collaborate with real-time developers for pipeline integration.

• Document model architecture decisions, training-data provenance, evaluation metrics and known limitations.

• Maintain version control and reproducibility for all training pipelines and model artifacts.