Mid ML Engineer

Luxoft Poland; Poland; On-site; PLN 120,000 - 190,000; Full time

Benefits offered by this job

Private Medical

Dental care

Life Insurance

Internal Mobility

Job summary

Luxoft Poland is seeking a seasoned ML Engineer to design, train, and evaluate classifiers using CPU PMC telemetry for security threat detection on AMD platforms. You will work on a hardware-assisted platform that blends ML with silicon-level counters and on-chip inference.

The role covers data engineering, model optimization for GPU/NPU inference, dataset expansion across malware variants, and rigorous documentation.

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

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 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 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.

Skills

3+ years in ML / data science

Python

ML model evaluation

Binary/multi-class classification

Education

Tools

PyTorch

TensorFlow

scikit-learn

ONNX

Job description

Private Medical & Dental care & Life Insurance

Internal Mobility program - possibility of rotation between projects, locations, accounts

Project 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.

Skills

What is relevant to have

• 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.

What is nice to have

• 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.