ML Engineer - Model Training

Luxoft Poland; Poland; On-site; PLN 180,000 - 240,000; Full time

Benefits offered by this job

Private Medical & Dental care & Life保险

MyBenefit program (sports card, well‑n

Internal Mobility program

Job summary

Luxoft Poland is seeking an ML Engineer to lead model development and validation for a hardware-assisted security platform.

The role focuses on feature engineering, classifier development, and runtime integration, with remote work available from Poland. You will collaborate with Lab and Real-Time teams to advance silicon-level threat detection and ML inference optimization.

Qualifications

• 4+ years of industry experience in applied machine learning, ML engineering, or data science.
• Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow or scikit-learn.
• Experience designing, training, and evaluating binary or multi-class classification models.
• Experience with tabular, time-series, event, sensor, telemetry or other structured numerical data.
• Solid understanding of model evaluation and validation metrics (precision, recall, F1, ROC-AUC, etc.).
• Experience with feature engineering, data preparation, experiment design, and iterative model improvement.
• Understanding of model optimization for inference: quantization, pruning, ONNX export.
• Ability to document model decisions, evaluation results, data assumptions, and configurations.
• Ability to collaborate with software and systems engineers on runtime integration.
• University degree in computer science, electrical/computer engineering, data science, mathematics, or equivalent.

Responsibilities

• Design, train, and evaluate machine-learning classifiers using CPU behavioural telemetry.
• Perform feature engineering on hardware performance-counter data and related features.
• Frame and label datasets, select features, and set sampling/windowing parameters.
• Develop evaluation frameworks covering precision, recall, F1, ROC-AUC, and latency.
• Analyze model behavior across workloads and threat variants; improve accuracy and robustness.
• Evaluate classifiers and anomaly-detection approaches for limited or imbalanced data.
• Quantize and optimize models for efficient inference on GPU/NPUs; balance detection quality and latency.
• Export models to production formats and integrate with runtime teams.
• Define experiments, compare architectures, document decisions and limitations.
• Maintain reproducibility for training pipelines, datasets, and model artifacts.

Skills

Python

PyTorch

TensorFlow

scikit-learn

ML engineering

Data science

Model evaluation

Feature engineering

Inference optimization

ONNX export

Education

Bachelor's degree in computer science / electrical / computer engineering / data science / mathematics

Tools

ONNX

Git

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, including ransomware, fileless malware, and cryptojacking, at the processor layer, below OS-based evasion.

The platform collects CPU behavioral telemetry, classifies it through an ML inference engine, and exposes threat signals to security-software partners through a standardized API.

The project covers the full engineering path from silicon telemetry and data generation through ML training and validation, real-time inference, lab qualification, and partner integration.

The ML Engineer will lead the model-development and validation workstream, covering feature engineering, classifier development, model evaluation, inference optimization, and collaboration with systems engineers on runtime integration.

The role can be performed remotely from anywhere in Poland. Additional implementation details will be shared during the recruitment process in line with the applicable confidentiality requirements.

Responsibilities:

Design, train, and evaluate machine-learning classifiers using CPU behavioral telemetry, with an initial focus on distinguishing malicious and benign activity.

Perform feature engineering on hardware performance-counter data, including branch behavior, cache-miss patterns, instruction-mix ratios, and other processor-level measurements.

Frame and label datasets, select relevant input features, and determine suitable 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, identify coverage gaps, and iteratively improve accuracy and robustness.

Evaluate classification and anomaly-detection approaches, including methods suitable for limited or imbalanced malicious-data scenarios.

Optimize and quantize models for efficient inference on GPU or NPU hardware while balancing detection quality, latency, model size, and system overhead.

Export models to production-compatible inference formats and collaborate with real-time and systems engineers on runtime integration.

Define experiments, compare model architectures, and document the rationale behind feature, model, threshold, and operating-point decisions.

Document training-data provenance, model architecture, evaluation results, operating parameters, known limitations, and reproducibility requirements.

Maintain version control and reproducibility for training pipelines, experiment configurations, datasets, and model artifacts.

Work closely with the Lab Engineer, Real-Time Developer, and Technical Team Lead to align data collection, model development, and end-to-end platform validation.

Mandatory Skills Description:

4+ years of industry experience in applied machine learning, machine-learning engineering, or data science.

Strong proficiency in Python and hands‑on experience with at least one major ML framework, such as PyTorch, TensorFlow, or scikit‑learn.

Practical experience designing, training, and evaluating binary or multi‑class classification models.

Experience working with tabular, time‑series, event, sensor, telemetry, or other structured numerical data.

Solid 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 model optimization for inference, 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 clearly 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.

(Opportunity for Poland-based candidates)

Tax‑deductible costs on a contract of employment for all development roles

Stable employment based on an employment contract

Private Medical & Dental care & Life Insurance

MyBenefit program (sports card, well‑being program etc.)

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