Senior Machine Learning System Builder

Our client is a global leader in cybersecurity for IT, OT, and ICS critical infrastructure. Their end-to-end platform gives enterprises and public sector organizations the critical advantage they need to protect complex networks, secure devices, and meet compliance requirements.

Over the past 20 years, a consistent commitment to innovative technology has earned the trust of more than 1,700 organizations, governments, and institutions worldwide — cementing the company's role in protecting the world's critical infrastructure and securing our way of life.

On their behalf, we are looking for a Sr. ML System Builder ready to contribute to that mission.

As a SeniorMachine Learning Systems Builder you own detection capabilities end to end: from problem framing and data, through experimentation, to models running in production and the telemetry that proves they work for customers. We've had a strong presence in Veszprém for over a decade, and we're now expanding into Budapest, this role is based in our newly opening Budapest office, right at the start of that growth.

You Will Have an Opportunity to

• Own detection capabilities from idea to customer impact across the AI/ML detection portfolio: threat similarity search (behavioral, code-structure, and static features), URL reputation, image-based phishing and brand-spoofing detection, web threat classification, and content classification
• Build and maintain the data pipelines your models depend on: sample sourcing and collection, ground truth and labeling workflows, feature extraction, and versioned training and evaluation datasets
• Design, train, fine-tune, and evaluate models, and see them through to production: versioned, runtime-portable artifacts (e.g., ONNX) consumed from the JVM-based backend, with input/output specifications, performance benchmarks, and documented limitations
• Build an automated model build and release pipeline, in the likes of SageMaker Pipelines: reproducible training runs, automated evaluation gates, and versioned artifact publishing, so that retraining and releasing a model is a routine operation rather than a project
• Own model quality in production, not just at release: telemetry feedback loops, false positive escalations, drift monitoring, and retraining cadence
• Design evaluation methodology for an adversarial, drifting domain: time-split validation, strict false positive ceilings, and robustness against evasion
• Automate the ML lifecycle with AI: use AI-assisted development daily, and build agentic automation for repetitive work such as labeling assistance, evaluation runs, regression testing, and reporting
• Set your own experimentation roadmap, prioritized by measurable customer-facing detection gains, and share what you learn openly across the team

Requirements

• 3+ years applied ML experience across at least two of: text/content classification, computer vision, similarity search / embedding models, security or threat detection
• Evidence of end-to-end delivery: models you personally took from data to production, running in systems used by other teams or customers
• Experience building data pipelines for model training: data collection, labeling and ground truth management, feature extraction, dataset versioning
• Strong Python; PyTorch or TensorFlow; experience with model evaluation frameworks
• Solid grounding in statistics and experiment design, including evaluation under distribution shift
• Comfort working across the integration boundary (for example, ONNX consumption from JVM services) rather than stopping at the model artifact
• Fluency with AI coding and agent tooling, and a track record of automating your own workflow with it
• Excellent communication and collaboration abilities; fluent in English

Benefits

• Stable, growing international company background with an exceptional customer group
• Opportunity to improve your professional skills
• The newest technology environment
• Language course and opportunity for active recreation – kettlebell, football and office massage
• Attractive working environment – nice office full of accessories (fruits every day, coffee, breakfast, tea etc.)
• Regular team events and Happy Hour activities