ML Engineer

Summary

Data has never been more valuable and vulnerable. As cybercriminals become more sophisticated and regulations more strict, organizations struggle to answer one key question: “Is my data safe?"

At Varonis, we see the world of cybersecurity differently. Instead of chasing threats, we believe the most practical approach is protecting data from the inside out. We’ve built the industry’s first fully autonomous Data Security Platform to help our customers dramatically reduce risk with minimal human effort.

At Varonis, we move fast. We’re an ultra-collaborative company with brilliant people who care deeply about the details. Together, we’re solving interesting and complex puzzles to keep the world’s data safe.

We work in a flexible, hybrid model, so you can choose the home-office balance that works best for you.

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.
• Partner with data scientists, cybersecurity researchers and product teams to evaluate and refine ML-driven capabilities.
• Conduct experiments and benchmark results in fast-paced, data-intensive environments.
  
  

• 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).
• 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.
  
  

• 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).