Senior Machine Learning System Builder

About this role

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