ML Engineer - Model Training

Luxoft Poland; Poland; On-site; PLN 180,000 - 340,000; Full time

Benefits offered by this job

Tax-deductible contract costs

Private Medical & Dental care & Life保险

MyBenefit program

Internal Mobility program

Job summary

Luxoft Poland is seeking a senior ML engineer to define and lead the ML model roadmap for a security platform that uses silicon-level PMCs and on-chip ML. You will architect scalable training pipelines, drive dataset strategy, and mentor engineers across projects.

The role involves collaborating with hardware teams on NPU capabilities, optimizing classifiers for throughput/latency, and presenting strategy in architecture reviews and partner meetings. Poland-based candidates encouraged.

Qualifications

• 7+ years of applied ML experience including 3+ years in security, anomaly detection or hardware/systems ML.
• Expert-level PyTorch or TensorFlow; strong Python; experience taking ML models to production.
• Experience with model deployment on GPU, NPU or other specialized accelerators.

Responsibilities

• Define and lead the ML model roadmap, progressing from binary malware/benign classification through multi-class threat taxonomy to behavioral attack-pattern detection.
• Architect training pipelines that scale to a growing malware variant library with reproducible, versioned experiments; establish model quality gates for production promotion.
• Lead research into advanced detection techniques including behavioral sequence modeling and detection of novel, previously unseen threat categories.
• Optimize multi-class ML classifiers for NPU inference against throughput and latency requirements; collaborate with hardware teams on NPU capability requirements.
• Drive dataset strategy including coverage across threat categories, synthetic data generation and dataset quality standards.
• Mentor MTS ML engineers; lead model and code reviews; establish best practices for reproducibility, documentation and experimental rigor. Represent ML model strategy in architecture reviews, external partner technical meetings and potential research publications.

Skills

ML leadership

Security ML

Team mentorship

Technical direction

Tools

PyTorch

TensorFlow

Python

GPU/NPU deployment

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:

Define and lead the ML model roadmap, progressing from binary malware/benign classification through multi-class threat taxonomy to behavioral attack-pattern detection.

Architect training pipelines that scale to a growing malware variant library with reproducible, versioned experiments; establish model quality gates for production promotion.

Lead research into advanced detection techniques including behavioral sequence modeling and detection of novel, previously unseen threat categories.

Optimize multi-class ML classifiers for NPU inference against throughput and latency requirements; collaborate with hardware teams on NPU capability requirements.

Drive dataset strategy including coverage across threat categories, synthetic data generation and dataset quality standards.

Mentor MTS ML engineers; lead model and code reviews; establish best practices for reproducibility, documentation and experimental rigor. Represent ML model strategy in architecture reviews, external partner technical meetings and potential research publications.

Mandatory Skills Description:

7+ years of applied ML experience, including 3+ years in security, anomaly detection or hardware/systems ML.

Expert-level proficiency in PyTorch or TensorFlow; strong Python; experience leading ML platform or infrastructure decisions. Demonstrated record of taking ML models from research to production deployment.

Experience with model deployment on GPU, NPU or other specialized hardware accelerators.

Ability to lead technical direction and influence cross-functional teams.

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