ml engineer for endpoint threat detection

AMD is building a hardware-assisted security platform that uses processor-level performance monitoring and machine learning to detect advanced endpoint threats, including ransomware, fileless malware, and cryptojacking. The platform analyzes CPU behavioral telemetry, classifies threats through an ML inference engine, and provides threat signals to security-software partners through a standardized API.

• Design, train, and evaluate machine-learning classifiers using CPU behavioral telemetry;
• Perform feature engineering on hardware performance-counter data;
• Frame and label datasets, select input features, and determine sampling and windowing parameters;
• Develop evaluation frameworks covering precision, recall, F1 score, ROC-AUC, false-positive rate, detection performance, and inference latency;
• Analyze model behavior across representative workloads and threat variants and improve accuracy and robustness;
• Evaluate classification and anomaly-detection approaches for limited or imbalanced malicious-data scenarios;
• Optimize and quantize models for efficient inference on GPU or NPU hardware;
• Export models to production-compatible inference formats and collaborate on runtime integration;
• Define experiments, compare model architectures, and document decisions;
• Document training-data provenance, model architecture, evaluation results, operating parameters, limitations, and reproducibility requirements;
• Maintain version control and reproducibility for training pipelines, experiment configurations, datasets, and model artifacts;
• Work with the Lab Engineer, Real-Time Developer, and Technical Team Lead on data collection, model development, and platform validation.

• 4+ Years of industry experience in applied machine learning, machine-learning engineering, or data science;
• Strong Python proficiency and hands-on experience with PyTorch, TensorFlow, scikit-learn, or another major ML framework;
• Practical experience designing, training, and evaluating binary or multi-class classification models;
• Experience with tabular, time-series, event, sensor, telemetry, or other structured numerical data;
• Understanding of model evaluation and validation, including cross-validation, precision, recall, F1 score, ROC-AUC, class imbalance, threshold selection, and false-positive analysis;
• Experience with feature engineering, data preparation, experiment design, and iterative model improvement;
• Understanding of inference optimization, including quantization, pruning, ONNX export, or equivalent techniques;
• Experience taking ML work beyond exploratory notebooks into reproducible engineering workflows or production-oriented environments;
• Ability to document model decisions, evaluation results, data assumptions, experiment configurations, and known limitations;
• Ability to cooperate with software and systems engineers on model integration and runtime constraints;
• University degree in computer science, electrical engineering, computer engineering, data science, mathematics, or an equivalent field;
• English at C1 Advanced level;
• Nice to have: anomaly detection, novelty detection, outlier detection, one-class classification, cybersecurity, malware analysis, endpoint threat detection, fraud detection, behavioral analytics, hardware performance counters, system telemetry, processor profiling, sequential telemetry, GPU/NPU/edge/embedded deployment, ONNX, ONNX Runtime, OpenVINO, TensorRT, TensorFlow Lite, model interpretability, imbalanced datasets, synthetic data, dataset shift, processor architecture, system performance, hardware/software interaction, research-to-production experience.

• Relocation options are available;
• Annual holiday of 20 or 26 days depending on overall seniority;
• Occasional leave of 1 or 2 days depending on the circumstances;
• Child care leave of 2 days or 16 hours per year;
• Absence due to force majeure of 2 days or 16 hours per year;
• Maternity Leave of 20 weeks;
• Parental Leave of 41 weeks;
• Paternity Leave of 14 days;
• Private healthcare insurance with unlimited access to specialists;
• Full dental support;
• Travel insurance;
• Life insurance and family coverage options;
• Reimbursement for corrective glasses;
• Multisport card and other wellbeing benefits;
• Preferential banking and car leasing offers;
• Cafeteria program discounts;
• Luxoft Social Benefit Fund support;
• Access to technical and soft-skills training, self-learning libraries, cloud programs, mentorship, and leadership programs;
• Rotation between projects and accounts and new career opportunities;
• Benefits depend on the form of cooperation and apply to employees under a contract of employment.