Senior Machine Learning System Builder
Senior machine learning engineer owning cybersecurity detection capabilities end to end, from data pipelines and experimentation through production deployment and monitoring. Requires 3+ years of applied ML experience, strong Python and deep-learning skills, and demonstrated model delivery in customer-facing systems.
About the job
Responsibilities
- Own machine-learning detection capabilities from problem framing and data through experimentation, production deployment, and customer-impact telemetry.
- Work across threat similarity search, URL reputation, image-based phishing and brand-spoofing detection, web-threat classification, and content classification.
- Build and maintain data pipelines for sample collection, ground-truth and labeling workflows, feature extraction, and versioned training and evaluation datasets.
- Design, train, fine-tune, and evaluate models; deploy versioned, runtime-portable artifacts such as ONNX models consumed by JVM-based backends.
- Build automated model build and release pipelines with reproducible training runs, evaluation gates, and versioned artifact publishing.
- Monitor production model quality through telemetry feedback loops, false-positive escalations, drift monitoring, and retraining cadence.
- Design evaluation methodologies for adversarial and drifting domains, including time-split validation, false-positive ceilings, and evasion robustness.
- Use AI-assisted development and build agentic automation for labeling assistance, evaluation, regression testing, and reporting.
- Set an experimentation roadmap based on measurable customer-facing detection improvements and share findings across the team.
Requirements
- 3+ years of applied machine-learning experience across at least two of the following: text or content classification, computer vision, similarity search or embedding models, and security or threat detection.
- Demonstrated end-to-end delivery of models from data to production in systems used by other teams or customers.
- Experience building model-training data pipelines, including data collection, labeling, ground-truth management, feature extraction, and dataset versioning.
- Strong Python skills and experience with PyTorch or TensorFlow and model-evaluation frameworks.
- Solid grounding in statistics and experiment design, including evaluation under distribution shift.
- Ability to work across integration boundaries, including ONNX consumption from JVM services.
- Fluency with AI coding and agent tools, with experience automating development workflows.
- Excellent communication and collaboration skills; fluent English.
Skills
Python, PyTorch, TensorFlow, Onnx, Jvm, Sagemaker Pipelines, Computer Vision, Text Classification, Similarity Search, Embedding Models, Statistics, Experiment Design, Drift Monitoring, Dataset Versioning, Threat Detection
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