Varonis is seeking a skilled ML Engineer to design, build, and deploy advanced ML solutions that drive analytics, anomaly detection, and data classification across enterprise-scale environments.
Requirements
- Strong programming proficiency in Python
- Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Hands-on experience with LLMs, prompt engineering, vector embedding techniques, or related technologies.
- Proficiency with big data platforms like Databricks, PySpark, and cloud services (Azure, AWS)
- Experience with MLOps tools and deployment (CI/CD, containerization, Kubernetes, etc.).
- Experience with vector DBs, retrieval-augmented generation (RAG) frameworks like Langchain
- Solid analytical and debugging skills with ability to translate research insights into production code.
Responsibilities
- Design, build, and deploy ML models for user behavior analytics, anomaly detection, and data classification across enterprise environments.
- Collaborate with data scientists, software and data engineers to integrate ML models into production pipelines, cloud-native environments, on-premises, and Databricks workflows.
- Develop, fine-tune, and evaluate LLMs and prompt engineering solutions for data classification, labeling, and threat analysis features.
- Optimize models using techniques like distillation, quantization, and efficient data structures to boost performance and lower resource cost.
- Build high-performance data inputs using embeddings, vector databases, and distributed training frameworks.
- Manage model lifecycle and performance via MLOps best practices: monitoring, retraining, and deploying updates.
- Conduct experiments and benchmark results in fast-paced, data-intensive environments.
Other
- Bachelor’s degree in computer science, data science, or related field.
- 3+ years of experience in Machine Learning engineering or ML-adjacent roles (data science, MLOps, AI)
- Prior experience working on cybersecurity or data protection products
- Familiarity with user behavior-based threat detection, anomaly detection, or metadata analytics
- Statistical modeling and prompt evaluation ability (e.g., response coherency, relevance)
