ML Engineer

Luxoft; Poland; On-site; PLN 180,000 - 260,000; Full time

Job summary

AMD is building a hardware-assisted security platform that uses silicon-level PMCs and on-chip ML to detect endpoint threats. You will design, train and evaluate ML classifiers on CPU telemetry, focusing on ransomware, cryptomining and fileless malware targets.

You will perform feature engineering on raw hardware-counter data, optimize models for inference on AMD GPUs/NPUs within strict compute budgets, and export production-ready formats while documenting decisions and ensuring reproducibility.

Qualifications

• 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.
• Understanding of model evaluation: cross-validation, precision/recall, F1, ROC-AUC.
• Understanding of model optimization for inference: quantization, pruning, ONNX export.

Responsibilities

• Design, train and evaluate ML classifiers on CPU PMC telemetry datasets targeting ransomware, cryptomining, fileless malware and related threat categories.
• Perform feature engineering on raw hardware performance counter data 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 formats.
• Document model architecture decisions, training-data provenance, evaluation metrics and known limitations.
• Maintain version control and reproducibility for all training pipelines and model artifacts.

Skills

ML experience

Python

PyTorch

TensorFlow

scikit-learn

Binary/multi-class classification

Model evaluation

Quantization

Pruning

ONNX export

Tools

ONNX

Job 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.

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.

Mandatory Skills Description:

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.

Nice-to-Have Skills Description:

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.